Peptic ulcer disease is the most common ulcer of an area of the gastro- intestinal tract. The aim
of this study is to utilize soft computing techniques to manage uncertainty and imprecision in
measurements related to the size, shape of the abnormality. For this, we designed a fuzzy
inference system (FIS) which emulates the process of human experts in detection and analysis of
the peptic ulcer. The proposed approach models the vagueness and uncertainty associated to
measurements of small objects in low resolution images In this study, for the first time, we
applied soft computing technique based upon fuzzy inference system (FIS) for assessment of the
severity of the peptic ulcer. Performance results reveal the FIS with maximum accuracy of
98.1%, which reveals superiority of the approach. The intelligent FIS system can help medical
experts as a second reader for detection of the peptic ulcer in the decision making process and
consequently, improves the treatment process.
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEIJCSEIT Journal
Breast cancer is one of the leading cancers for women in developed countries including India. It is the
second most common cause of cancer death in women. The high incidence of breast cancer in women has
increased significantly in the last years. In this paper we have discussed various data mining approaches
that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is
distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
This study compared same day sputum microscopy (two sputum samples collected one hour apart) to conventional sputum microscopy (spot sample and early morning sample collected over two days) for tuberculosis diagnosis in Chhattisgarh, India. The study found that same day microscopy missed 17% of smear-positive tuberculosis cases compared to 1% missed by conventional microscopy. Additionally, same day microscopy had a lower proportion of presumptive tuberculosis patients providing both required samples and had a lower proportion of samples with good quality. These findings suggest that same day microscopy may not be as effective as conventional microscopy for tuberculosis diagnosis in this setting.
Case Based Medical Diagnosis of Occupational Chronic Lung Diseases From Their...CSCJournals
The clinical decision support system using the case based reasoning (CBR) methodology of Artificial Intelligence (AI) presents a foundation for a new technology of building intelligent computer aided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. In this paper, we have used the Case Based Reasoning methodology to develop a clinical decision support system prototype for supporting diagnosis of occupational lung diseases. 127 cases were collected for 14 occupational chronic lung diseases, which contains 26 symptoms. After removing the duplicated cases from the database, the system has trained set of 47 cases for Indian Lung patients. Statistical analysis has been done to determine the importance values of the case features. The retrieval strategy using nearest-neighbor approaches is investigated. The results indicate that the nearest neighbor approach has shown the encouraging outcome, used as retrieval strategy. A Consultant Pathologist’s interpretation was used to evaluate the system. Results for Sensitivity, Specificity, Positive Prediction Value and the Negative Prediction Value are 95.3%, 92.7%, 98.6% and 81.2% respectively. Thus, the result showed that the system is capable of assisting an inexperience pathologist in making accurate, consistent and timely diagnoses, also in the study of diagnostic protocol, education, self-assessment, and quality control. In this paper, clinical decision support system prototype is developed for supporting diagnosis of occupational lung diseases from their symptoms and signs through employing Microsoft Visual Basic .NET 2005 along with Microsoft SQL server 2005 environment with the advantage of Object Oriented Programming technology
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
This document discusses applying machine learning algorithms to predict chronic kidney disease. It:
1) Applied three algorithms (C4.5 decision tree, SVM, and Bayesian Network) to a chronic kidney disease dataset containing 400 patients and 24 attributes to classify patients as having chronic kidney disease or not.
2) Found that the C4.5 decision tree algorithm had the best performance based on accuracy (63%), error rate (0.37), kappa statistic (0.97), and other evaluation metrics. SVM and Bayesian Network performance was lower.
3) Concludes C4.5 decision tree is the most efficient algorithm for predicting chronic kidney disease based on this medical dataset.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Estimating the Statistical Significance of Classifiers used in the Predictio...IOSR Journals
This document summarizes a research paper that analyzes the statistical significance of different classifiers for predicting tuberculosis. The paper first compares the accuracy of classifiers like decision trees, support vector machines, k-nearest neighbor, and naive Bayes on tuberculosis data. It then evaluates the performance of these classifiers using a paired t-test to select the optimal model. The results showed that support vector machines and decision trees were not statistically significant, while support vector machines combined with naive Bayes and k-nearest neighbor were statistically significant.
In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we
formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a
framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done
by comparing expert knowledge and system generated response. This basic emblematic approach using
fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and
provides support platform to pulmonary researchers in identifying the ailment effectively.
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEIJCSEIT Journal
Breast cancer is one of the leading cancers for women in developed countries including India. It is the
second most common cause of cancer death in women. The high incidence of breast cancer in women has
increased significantly in the last years. In this paper we have discussed various data mining approaches
that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is
distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
This study compared same day sputum microscopy (two sputum samples collected one hour apart) to conventional sputum microscopy (spot sample and early morning sample collected over two days) for tuberculosis diagnosis in Chhattisgarh, India. The study found that same day microscopy missed 17% of smear-positive tuberculosis cases compared to 1% missed by conventional microscopy. Additionally, same day microscopy had a lower proportion of presumptive tuberculosis patients providing both required samples and had a lower proportion of samples with good quality. These findings suggest that same day microscopy may not be as effective as conventional microscopy for tuberculosis diagnosis in this setting.
Case Based Medical Diagnosis of Occupational Chronic Lung Diseases From Their...CSCJournals
The clinical decision support system using the case based reasoning (CBR) methodology of Artificial Intelligence (AI) presents a foundation for a new technology of building intelligent computer aided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. In this paper, we have used the Case Based Reasoning methodology to develop a clinical decision support system prototype for supporting diagnosis of occupational lung diseases. 127 cases were collected for 14 occupational chronic lung diseases, which contains 26 symptoms. After removing the duplicated cases from the database, the system has trained set of 47 cases for Indian Lung patients. Statistical analysis has been done to determine the importance values of the case features. The retrieval strategy using nearest-neighbor approaches is investigated. The results indicate that the nearest neighbor approach has shown the encouraging outcome, used as retrieval strategy. A Consultant Pathologist’s interpretation was used to evaluate the system. Results for Sensitivity, Specificity, Positive Prediction Value and the Negative Prediction Value are 95.3%, 92.7%, 98.6% and 81.2% respectively. Thus, the result showed that the system is capable of assisting an inexperience pathologist in making accurate, consistent and timely diagnoses, also in the study of diagnostic protocol, education, self-assessment, and quality control. In this paper, clinical decision support system prototype is developed for supporting diagnosis of occupational lung diseases from their symptoms and signs through employing Microsoft Visual Basic .NET 2005 along with Microsoft SQL server 2005 environment with the advantage of Object Oriented Programming technology
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
This document discusses applying machine learning algorithms to predict chronic kidney disease. It:
1) Applied three algorithms (C4.5 decision tree, SVM, and Bayesian Network) to a chronic kidney disease dataset containing 400 patients and 24 attributes to classify patients as having chronic kidney disease or not.
2) Found that the C4.5 decision tree algorithm had the best performance based on accuracy (63%), error rate (0.37), kappa statistic (0.97), and other evaluation metrics. SVM and Bayesian Network performance was lower.
3) Concludes C4.5 decision tree is the most efficient algorithm for predicting chronic kidney disease based on this medical dataset.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Estimating the Statistical Significance of Classifiers used in the Predictio...IOSR Journals
This document summarizes a research paper that analyzes the statistical significance of different classifiers for predicting tuberculosis. The paper first compares the accuracy of classifiers like decision trees, support vector machines, k-nearest neighbor, and naive Bayes on tuberculosis data. It then evaluates the performance of these classifiers using a paired t-test to select the optimal model. The results showed that support vector machines and decision trees were not statistically significant, while support vector machines combined with naive Bayes and k-nearest neighbor were statistically significant.
In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we
formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a
framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done
by comparing expert knowledge and system generated response. This basic emblematic approach using
fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and
provides support platform to pulmonary researchers in identifying the ailment effectively.
Digital Pathology, FDA Approval and Precision MedicineJoel Saltz
Digital pathology platforms combined with machine learning can improve the consistency and quality of clinical decision making by precisely scoring known criteria from pathology images and predicting treatment outcomes and cancer types. Researchers are developing tools to extract features from pathology images, link these features to molecular data and clinical outcomes, and use these integrated datasets to gain new insights into cancer and select the best interventions. The SEER Virtual Tissue Repository aims to enable population-level cancer research by creating a linked collection of de-identified clinical data and whole slide images from pathology samples that can be analyzed using computational methods.
Automated weaning systems aim to improve adaptation of mechanical ventilation support based on continuous patient monitoring. This systematic review and meta-analysis evaluated 21 randomized controlled trials comparing automated weaning systems to non-automated weaning. Pooled results found that automated systems reduced the duration of mechanical ventilation by 10% and time spent in the intensive care unit by 8%. Automated systems also decreased weaning duration by 30%, with the greatest effect seen in mixed or medical intensive care unit populations and when using the Smartcare/PSTM system. There was no strong evidence of impact on mortality or hospital length of stay. Overall, automated weaning systems can reduce ventilation and intensive care unit times.
K-Nearest Neighbours based diagnosis of hyperglycemiaijtsrd
This document summarizes a research paper that developed an artificial intelligence system using the K-nearest neighbors algorithm to diagnose hyperglycemia (high blood sugar). The system was trained on a database of 415 patient cases characterized by 10 physiological parameters. It achieved a diagnostic accuracy of 91% compared to medical experts when tested on new patient data. The authors conclude the KNN-based system is useful for diabetes diagnosis and could help supplement medical doctors, especially in remote areas with limited access to experts.
1) A digital therapeutic called reSET was the first to receive FDA approval as a prescription digital treatment for substance abuse disorders like alcohol, cocaine, and marijuana addiction. Clinical trials showed patients using reSET had statistically significant increased odds of abstinence compared to a control group.
2) The study evaluated older adults ages 60-85 who played the multitasking video game NeuroRacer. Those who received multitasking training showed reduced multitasking costs compared to control groups, performing better than untrained 20-year-olds. The training also improved neural signatures of cognitive control and benefits extended to untrained cognitive abilities.
3) Digital therapeutics can deliver evidence-based treatments through software to prevent, manage
LIVER DISEASE PREDICTION BY USING DIFFERENT DECISION TREE TECHNIQUESIJDKP
Early prediction of liver disease is very important to save human life and take proper steps to control the
disease. Decision Tree algorithms have been successfully applied in various fields especially in medical
science. This research work explores the early prediction of liver disease using various decision tree
techniques. The liver disease dataset which is select for this study is consisting of attributes like total
bilirubin, direct bilirubin, age, gender, total proteins, albumin and globulin ratio. The main purpose of this
work is to calculate the performance of various decision tree techniques and compare their performance.
The decision tree techniques used in this study are J48, LMT, Random Forest, Random tree, REPTree,
Decision Stump, and Hoeffding Tree. The analysis proves that Decision Stump provides the highest
accuracy than other techniques
Study on Physicians Request for Computed Tomography Examinations for Patients...IRJESJOURNAL
Background and objectives: There is a lot controversy about the use of Computed tomography (CT) for patients with minor head injury. We aimed to determine the practice of guiding rules for the safety of radiation and increasing awareness of physicians about risks of ionizing radiation and find out the reasons of emergency doctors for sending head injury patients to CT scan exams. Materials and Methods: A descriptive questionnaire in the Emergency Department (ED) based study was performed to assess physicians' knowledge of radiation doses received from radiological treatments and knowledge about Clinic Decision Support rules (CDS). The questionnaire consisted of 26 questions distributed to physicians working in the emergency department in six hospitals in East Java. Finally, the data collected have been analyzed by some tests using SPSS version 15 and Smart PLS. Results: In this study 44 participants had taken part. The percentage of general knowledge and awareness that shows the response of people who work in the emergency departments was total 44 respondents, by percent 6.8% of the respondents had passably knowledge, awareness and 84.1% they were having a good knowledge and awareness and 9.1% the respondents had very good knowledge and awareness. That means almost of respondents have good knowledge and awareness. To find out if an indicator is forming a construct (latent variables) testing the convergent validity of the measurement model with a reflexive indicator assessed based on the correlation between the item score to construct scores were calculated with the help of software Smart PLS. Size reflexive considered valid if the individual has a correlation (loading) to construct (latent variables) to be measured ≥ 0.5 or the value of t-statistics should ≥1.96 (test two tailed) at a significance level of α = 0.05. If one of the indicators has a leading value <0.5,><1.96, then the indicator should be discarded (dropped) because it indicates that the indicators are not good enough to measure the construct in right. The positive influence between general knowledge and awareness against to knowledge about radiation doses can be interpreted that the better general knowledge and awareness, then it will be followed by an increase in their knowledge about radiation doses. And vice versa, the worse general knowledge and awareness, then this will decrease their knowledge about radiation doses too. Conclusion: The present study has illustrated that the level of awareness and knowledge physicians who deal with ionizing radiation in CT scan units are adequate overall. There is a good influence between the diligence in applying the principles of guidance and rules stipulated by the nuclear energy in Indonesia by physicians to adjust the use of CT in the emergency department, the majority of participants who have a good awareness & knowledge, there are some of them do not have enough knowledge.
1. A growing number of early lesions that are difficult to diagnose leads to over or under calling diagnoses, resulting in inappropriate patient management. 2. Introducing standardized diagnostic criteria that are configurable and can be uniformly applied to all cases through a synoptic format helps ensure consistent diagnosis. 3. Linking medical literature and images to the criteria allows for ongoing teaching, validation and calibration of pathologists, helping to prospectively avoid diagnostic errors.
This study compared the effectiveness of same-day light-emitting diode fluorescence microscopy (LED-FM) to the conventional method for diagnosing tuberculosis (TB) in Chhattisgarh, India. Of over 1700 presumptive TB patients who provided all three sputum samples, 218 (13%) were smear-positive. The same-day method identified 200 cases (11.7%) while the conventional method identified 217 cases (12.7%). The same-day method missed 18 cases (8.3%) that were identified by the conventional method. While LED-FM is more sensitive, these findings suggest that some smear-positive cases may be missed using the same-day diagnosis method compared to the conventional
Cancer prognosis prediction using balanced stratified samplingijscai
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
1. Deep learning is being used in medicine for tasks like classification, segmentation, and detection using convolutional neural networks. Google has developed algorithms for diabetic retinopathy detection and cancer metastasis detection with high accuracy.
2. Unsupervised learning techniques like generative adversarial networks show promise for generating medical images but have challenges around validation.
3. Concerns with deep learning in medicine include the need for large labeled datasets, validating models across different patient populations and settings, legal and responsibility issues, and discrepancies between clinical and general populations.
4. Future areas of focus include generative adversarial networks and reinforcement learning. Cooperation between researchers and doctors will be important to address challenges around credibility and validation of deep learning models
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
Performance Analysis of Data Mining Methods for Sexually Transmitted Disease ...IJECEIAES
According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.
A Qualitative Study to Understand the Barriers and Enablers in implementing a...Vojislav Valcic MBA
The document summarizes a qualitative study that explored barriers and enablers to implementing an Enhanced Recovery After Surgery (ERAS) program across several hospitals affiliated with the University of Toronto. Semistructured interviews were conducted with surgeons, anesthesiologists, and nurses. The interviews identified several common barriers, including lack of resources, poor communication, resistance to change, and patient factors. However, interviewees generally supported implementing a standardized ERAS program with guidelines based on evidence, education of staff and patients, and standardized order sets. Identifying these barriers and enablers is an important first step to successfully adopting an ERAS program.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Emergency Department Triage and Digital HealthHughSingleton
This document discusses the use of digital health and machine learning in emergency department triage. It reviews 5 research articles on this topic that use machine learning algorithms like random forest and deep learning to more accurately predict patient outcomes like critical care need, hospitalization, and mortality compared to traditional triage methods. The studies demonstrate that machine learning can reduce under-triage of critically ill patients and over-triage of less ill patients. The author concludes that machine learning and deep learning offer opportunities to improve triage accuracy and potentially be incorporated directly into triage systems.
This randomized clinical trial compared medication administration error rates between dedicated medication nurses and general nurses across two hospitals. The main findings were:
1) Overall error rates were similar between medication nurses (15.7%) and general nurses (14.9%).
2) At one hospital, medication nurses had a significantly lower error rate than general nurses in surgical units but not medical units.
3) Differences in medication processes and settings highlighted the role of systems design in errors. The study suggests simple interventions may not reduce errors without broader system changes.
This document summarizes a study on drug utilization patterns in patients with burns over 15% of their total body surface area admitted to a tertiary hospital burn ward in Nashik, India. A total of 50 patients were included in the study, with an average burn percentage of 61.96%. The mortality rate was found to be 20% for burns under 40% TBSA, 33% for 40-60% TBSA, and 95% for over 60% TBSA. The most commonly prescribed drugs were Ringer's Lactate, gentamicin, ranitidine, metronidazole, cefoperazone + sulbactam, and ciprofloxacin. The drug utilization 90% included
This document discusses conceptual literature related to network monitoring and diagnosis systems. It provides definitions and descriptions of key concepts from various sources, including network monitoring, bandwidth, uptime, downtime, uploading/downloading, user sessions, CPU utilization, fault detection, peer-to-peer networking, network interface cards, routers, network flows, and the strategic importance of network monitoring. Diagrams and tables are included to illustrate several of these concepts.
This document provides an overview of the research process and guidelines for reviewing related literature. It discusses the importance of reviewing related literature to avoid duplicating past studies and provide context for the research problem. The review of related literature should include recent, objective materials that are directly relevant to the study, such as findings, methods, and conclusions from past investigations. When writing the literature review, the researcher should paraphrase sources, cite authors, include only relevant information, and relate sources to the research topic. The review should have a logical flow and avoid lengthy quotes or a list-like format.
The document provides a literature review on import products in hardware establishments. It discusses foreign literature on international trade and how trade differs between domestic and international markets. Local literature examines issues like parallel imports in the pharmaceutical industry and import substitution strategies. The theoretical framework discusses the conditionally-free import theory. There are also definitions of key terms like importing problems and ownership knowledge.
Chapter 2-Realated literature and StudiesMercy Daracan
This chapter reviews related literature and studies relevant to the present study. It discusses ideas from local and foreign sources on the importance of computer information technology and information systems. It also examines related theories like the iterative implementation approach and discusses how technologies like WAMP5, Windows 7, and web-based systems have influenced various fields and processes like enrollment. Finally, it summarizes some related local studies that have developed web-based enrollment systems to make the enrollment process more efficient.
Digital Pathology, FDA Approval and Precision MedicineJoel Saltz
Digital pathology platforms combined with machine learning can improve the consistency and quality of clinical decision making by precisely scoring known criteria from pathology images and predicting treatment outcomes and cancer types. Researchers are developing tools to extract features from pathology images, link these features to molecular data and clinical outcomes, and use these integrated datasets to gain new insights into cancer and select the best interventions. The SEER Virtual Tissue Repository aims to enable population-level cancer research by creating a linked collection of de-identified clinical data and whole slide images from pathology samples that can be analyzed using computational methods.
Automated weaning systems aim to improve adaptation of mechanical ventilation support based on continuous patient monitoring. This systematic review and meta-analysis evaluated 21 randomized controlled trials comparing automated weaning systems to non-automated weaning. Pooled results found that automated systems reduced the duration of mechanical ventilation by 10% and time spent in the intensive care unit by 8%. Automated systems also decreased weaning duration by 30%, with the greatest effect seen in mixed or medical intensive care unit populations and when using the Smartcare/PSTM system. There was no strong evidence of impact on mortality or hospital length of stay. Overall, automated weaning systems can reduce ventilation and intensive care unit times.
K-Nearest Neighbours based diagnosis of hyperglycemiaijtsrd
This document summarizes a research paper that developed an artificial intelligence system using the K-nearest neighbors algorithm to diagnose hyperglycemia (high blood sugar). The system was trained on a database of 415 patient cases characterized by 10 physiological parameters. It achieved a diagnostic accuracy of 91% compared to medical experts when tested on new patient data. The authors conclude the KNN-based system is useful for diabetes diagnosis and could help supplement medical doctors, especially in remote areas with limited access to experts.
1) A digital therapeutic called reSET was the first to receive FDA approval as a prescription digital treatment for substance abuse disorders like alcohol, cocaine, and marijuana addiction. Clinical trials showed patients using reSET had statistically significant increased odds of abstinence compared to a control group.
2) The study evaluated older adults ages 60-85 who played the multitasking video game NeuroRacer. Those who received multitasking training showed reduced multitasking costs compared to control groups, performing better than untrained 20-year-olds. The training also improved neural signatures of cognitive control and benefits extended to untrained cognitive abilities.
3) Digital therapeutics can deliver evidence-based treatments through software to prevent, manage
LIVER DISEASE PREDICTION BY USING DIFFERENT DECISION TREE TECHNIQUESIJDKP
Early prediction of liver disease is very important to save human life and take proper steps to control the
disease. Decision Tree algorithms have been successfully applied in various fields especially in medical
science. This research work explores the early prediction of liver disease using various decision tree
techniques. The liver disease dataset which is select for this study is consisting of attributes like total
bilirubin, direct bilirubin, age, gender, total proteins, albumin and globulin ratio. The main purpose of this
work is to calculate the performance of various decision tree techniques and compare their performance.
The decision tree techniques used in this study are J48, LMT, Random Forest, Random tree, REPTree,
Decision Stump, and Hoeffding Tree. The analysis proves that Decision Stump provides the highest
accuracy than other techniques
Study on Physicians Request for Computed Tomography Examinations for Patients...IRJESJOURNAL
Background and objectives: There is a lot controversy about the use of Computed tomography (CT) for patients with minor head injury. We aimed to determine the practice of guiding rules for the safety of radiation and increasing awareness of physicians about risks of ionizing radiation and find out the reasons of emergency doctors for sending head injury patients to CT scan exams. Materials and Methods: A descriptive questionnaire in the Emergency Department (ED) based study was performed to assess physicians' knowledge of radiation doses received from radiological treatments and knowledge about Clinic Decision Support rules (CDS). The questionnaire consisted of 26 questions distributed to physicians working in the emergency department in six hospitals in East Java. Finally, the data collected have been analyzed by some tests using SPSS version 15 and Smart PLS. Results: In this study 44 participants had taken part. The percentage of general knowledge and awareness that shows the response of people who work in the emergency departments was total 44 respondents, by percent 6.8% of the respondents had passably knowledge, awareness and 84.1% they were having a good knowledge and awareness and 9.1% the respondents had very good knowledge and awareness. That means almost of respondents have good knowledge and awareness. To find out if an indicator is forming a construct (latent variables) testing the convergent validity of the measurement model with a reflexive indicator assessed based on the correlation between the item score to construct scores were calculated with the help of software Smart PLS. Size reflexive considered valid if the individual has a correlation (loading) to construct (latent variables) to be measured ≥ 0.5 or the value of t-statistics should ≥1.96 (test two tailed) at a significance level of α = 0.05. If one of the indicators has a leading value <0.5,><1.96, then the indicator should be discarded (dropped) because it indicates that the indicators are not good enough to measure the construct in right. The positive influence between general knowledge and awareness against to knowledge about radiation doses can be interpreted that the better general knowledge and awareness, then it will be followed by an increase in their knowledge about radiation doses. And vice versa, the worse general knowledge and awareness, then this will decrease their knowledge about radiation doses too. Conclusion: The present study has illustrated that the level of awareness and knowledge physicians who deal with ionizing radiation in CT scan units are adequate overall. There is a good influence between the diligence in applying the principles of guidance and rules stipulated by the nuclear energy in Indonesia by physicians to adjust the use of CT in the emergency department, the majority of participants who have a good awareness & knowledge, there are some of them do not have enough knowledge.
1. A growing number of early lesions that are difficult to diagnose leads to over or under calling diagnoses, resulting in inappropriate patient management. 2. Introducing standardized diagnostic criteria that are configurable and can be uniformly applied to all cases through a synoptic format helps ensure consistent diagnosis. 3. Linking medical literature and images to the criteria allows for ongoing teaching, validation and calibration of pathologists, helping to prospectively avoid diagnostic errors.
This study compared the effectiveness of same-day light-emitting diode fluorescence microscopy (LED-FM) to the conventional method for diagnosing tuberculosis (TB) in Chhattisgarh, India. Of over 1700 presumptive TB patients who provided all three sputum samples, 218 (13%) were smear-positive. The same-day method identified 200 cases (11.7%) while the conventional method identified 217 cases (12.7%). The same-day method missed 18 cases (8.3%) that were identified by the conventional method. While LED-FM is more sensitive, these findings suggest that some smear-positive cases may be missed using the same-day diagnosis method compared to the conventional
Cancer prognosis prediction using balanced stratified samplingijscai
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
1. Deep learning is being used in medicine for tasks like classification, segmentation, and detection using convolutional neural networks. Google has developed algorithms for diabetic retinopathy detection and cancer metastasis detection with high accuracy.
2. Unsupervised learning techniques like generative adversarial networks show promise for generating medical images but have challenges around validation.
3. Concerns with deep learning in medicine include the need for large labeled datasets, validating models across different patient populations and settings, legal and responsibility issues, and discrepancies between clinical and general populations.
4. Future areas of focus include generative adversarial networks and reinforcement learning. Cooperation between researchers and doctors will be important to address challenges around credibility and validation of deep learning models
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
Performance Analysis of Data Mining Methods for Sexually Transmitted Disease ...IJECEIAES
According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.
A Qualitative Study to Understand the Barriers and Enablers in implementing a...Vojislav Valcic MBA
The document summarizes a qualitative study that explored barriers and enablers to implementing an Enhanced Recovery After Surgery (ERAS) program across several hospitals affiliated with the University of Toronto. Semistructured interviews were conducted with surgeons, anesthesiologists, and nurses. The interviews identified several common barriers, including lack of resources, poor communication, resistance to change, and patient factors. However, interviewees generally supported implementing a standardized ERAS program with guidelines based on evidence, education of staff and patients, and standardized order sets. Identifying these barriers and enablers is an important first step to successfully adopting an ERAS program.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Emergency Department Triage and Digital HealthHughSingleton
This document discusses the use of digital health and machine learning in emergency department triage. It reviews 5 research articles on this topic that use machine learning algorithms like random forest and deep learning to more accurately predict patient outcomes like critical care need, hospitalization, and mortality compared to traditional triage methods. The studies demonstrate that machine learning can reduce under-triage of critically ill patients and over-triage of less ill patients. The author concludes that machine learning and deep learning offer opportunities to improve triage accuracy and potentially be incorporated directly into triage systems.
This randomized clinical trial compared medication administration error rates between dedicated medication nurses and general nurses across two hospitals. The main findings were:
1) Overall error rates were similar between medication nurses (15.7%) and general nurses (14.9%).
2) At one hospital, medication nurses had a significantly lower error rate than general nurses in surgical units but not medical units.
3) Differences in medication processes and settings highlighted the role of systems design in errors. The study suggests simple interventions may not reduce errors without broader system changes.
This document summarizes a study on drug utilization patterns in patients with burns over 15% of their total body surface area admitted to a tertiary hospital burn ward in Nashik, India. A total of 50 patients were included in the study, with an average burn percentage of 61.96%. The mortality rate was found to be 20% for burns under 40% TBSA, 33% for 40-60% TBSA, and 95% for over 60% TBSA. The most commonly prescribed drugs were Ringer's Lactate, gentamicin, ranitidine, metronidazole, cefoperazone + sulbactam, and ciprofloxacin. The drug utilization 90% included
This document discusses conceptual literature related to network monitoring and diagnosis systems. It provides definitions and descriptions of key concepts from various sources, including network monitoring, bandwidth, uptime, downtime, uploading/downloading, user sessions, CPU utilization, fault detection, peer-to-peer networking, network interface cards, routers, network flows, and the strategic importance of network monitoring. Diagrams and tables are included to illustrate several of these concepts.
This document provides an overview of the research process and guidelines for reviewing related literature. It discusses the importance of reviewing related literature to avoid duplicating past studies and provide context for the research problem. The review of related literature should include recent, objective materials that are directly relevant to the study, such as findings, methods, and conclusions from past investigations. When writing the literature review, the researcher should paraphrase sources, cite authors, include only relevant information, and relate sources to the research topic. The review should have a logical flow and avoid lengthy quotes or a list-like format.
The document provides a literature review on import products in hardware establishments. It discusses foreign literature on international trade and how trade differs between domestic and international markets. Local literature examines issues like parallel imports in the pharmaceutical industry and import substitution strategies. The theoretical framework discusses the conditionally-free import theory. There are also definitions of key terms like importing problems and ownership knowledge.
Chapter 2-Realated literature and StudiesMercy Daracan
This chapter reviews related literature and studies relevant to the present study. It discusses ideas from local and foreign sources on the importance of computer information technology and information systems. It also examines related theories like the iterative implementation approach and discusses how technologies like WAMP5, Windows 7, and web-based systems have influenced various fields and processes like enrollment. Finally, it summarizes some related local studies that have developed web-based enrollment systems to make the enrollment process more efficient.
The document provides guidelines for an oral defense of a thesis on an automated student record system at Surigao del Sur State University-Cagwait Campus. It includes instructions to highlight explanations in yellow and only present necessary parts of the thesis. It then summarizes in 1-2 sentences each chapter to be briefly explained, including the introduction, background of the study, statement of the problem, scope and limitations, objectives, and significance of the study. The chapters focus on designing a student record system using Microsoft Access to improve services and transactions by organizing student information and files in a secure automated process.
Literature Review (Review of Related Literature - Research Methodology)Dilip Barad
Literature Review or Review of Related Literature is one of the most vital stages in any research. This presentation attempts to throw some light on the process and important aspects of literature review.
The document describes a proposed hospital management system (HMS) that aims to automate and standardize a hospital's management processes. Currently, hospitals rely on manual paper-based systems that are inefficient and prone to errors. The HMS would control key information like patient data, schedules, and invoices electronically. It would make hospital management more efficient and reduce errors by standardizing data and ensuring integrity across information systems. The system design involves modules for registration, pharmacy, doctors, reception, laboratory, and discharge summaries. The technical requirements specify technologies like ASP.NET, C#, and SQL Server for development. UML diagrams including use cases, sequences, and classes are used for design. Data flow diagrams and entity-relationship diagrams model the
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
3. The system aims to assist radiologists by providing automated disease diagnoses from X-ray images to reduce their workload and enable diagnoses in remote areas.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
A Decision Support System for Tuberculosis Diagnosability ijsc
In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done by comparing expert knowledge and system generated response. This basic emblematic approach using fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and provides support platform to pulmonary researchers in identifying the ailment effectively.
AN IMPROVED MODEL FOR CLINICAL DECISION SUPPORT SYSTEMijaia
The document describes an improved model for a clinical decision support system that was developed to address issues with misdiagnosis and inconsistent healthcare records. The system incorporates both knowledge-based and non-knowledge based decision support methods using a hybrid approach. It was trained and validated using prostate cancer and diabetes datasets, achieving classification accuracies of 98% and 94% respectively. The system aims to enhance disease detection and prediction to support better healthcare delivery.
Therapeutic management of diseases based on fuzzy logic system- hypertriglyce...TELKOMNIKA JOURNAL
The support systems for assisting clinical decision highly improve the quality and efficiency of the therapeutic and diagnostic treatment in medicine. The proper implementation of such systems can emulate the reasoning of health care professionals in such a way that suggest reasonable decisions on patient treatment. The fuzzy logic system can be considered as one of the efficient techniques for converting a complex decision tree that usually facing the physician into artificial intelligent procedure embedded in a computer program. So many properties in fuzzy logic system that can facilitate the process of medical diagnosis and therapeutic management. In this paper, a system for therapeutic management of hypertriglyceridemia was efficiently realized using a fuzzy logic technique. The obtained results had shown that the proposed fuzzy logic contributes a reliable managing procedure for assisting the physicians and pharmacist in treating the hypertriglyceridemia. Many different hypertriglyceridemia treatment cases showed a perfect matching decision between the standard guidelines and that given by the proposed system.
Care expert assistant for Medicare system using Machine learningIRJET Journal
1) The document presents a machine learning-based care assistant system for the Medicare system. It allows for patient registration, storing patient details, and processing pharmacy and lab requests.
2) It uses machine learning algorithms like Naive Bayes and Support Vector Machine to predict diseases based on patient symptoms and vital signs. It generates a report with the predicted disease and provides treatment suggestions.
3) The system aims to provide remote healthcare access and improve national health outcomes. It allows doctors to remotely monitor and treat patients using the mobile application.
Automated Generation Of Synoptic Reports From Narrative Pathology Reports In ...Kaela Johnson
This document presents a study that developed a rule-based natural language processing (NLP) algorithm to automatically generate structured synoptic pathology reports from narrative breast pathology reports. The algorithm was trained on 415 pathology reports from University Malaya Medical Centre and evaluated on 178 additional reports. Key data elements were identified by a pathologist and the NLP algorithm used predefined rules and lists to extract these elements with high accuracy, achieving a micro-F1 score of 99.50% and macro-F1 score of 98.97% on the test set. The structured synoptic reports generated by the algorithm are intended to improve information extraction for clinical decision making and research.
Detecting and Preventing Ulcerative Colitis samples using efficient feature s...IRJET Journal
This document presents a machine learning approach to detecting and preventing ulcerative colitis (UC) using colonoscopy videos and patient data. The authors extract features from colonoscopy footage and medical records, then train an ensemble learning model using decision trees, K-nearest neighbors, naive Bayes, and support vector machines. The model analyzes normal values and predicts UC levels. It aims to help diagnose UC and increase patient awareness. Evaluation shows decision trees performed well for text data while support vector machines worked best for image data. The framework could help tackle UC given its impact on many people worldwide.
Structure and development of a clinical decision support system: application ...komalicarol
Clinical decision requires to infer great, diverse and not suitably
organized quantity of information and having low time to decide.
The therapeutic choice is fundamental to formulate a strategy to
avoid complications and to achieve favorable results, being more
important in some specialties. In addition, medical decision-makers are overloaded with clinical tasks, have an intense work rate and
are subject to a great demand, and are prone to greater tiredness.
In this sense, computer tools can be extremely useful, as can deal
with a lot of information in much less time than decision-makers.
Thus, the existence of a tool that assists them in decision-making
is of crucial importance
Efficient Fuzzy-Based System for the Diagnosis and Treatment of Tuberculosis ...Editor IJCATR
The aim of this study is to design a FuzzyBased Expert System for Tuberculosis diagnosis
and Treatment. The designed system made use of General Hospital Adikpo, patient database. The system
has 18 input fields and five outputs field. Input fields are Chest pain (CP), cough duration (CD), fever
duration (FV), night sweats (NS), weight loss (WL), loss of appetite (LOA), change in bowel habits
(CBH), variations in mental behaviour (VMB), masses along the neck (MAN), draining sinus (DS),
coma (seizure) (CO), stiff Neck (SN), headache (HD), abdominal Pain (AP), painful or uncomfortable
urination (PU), hemopysis (coughing up blood) (CUB), fatigue (FA) and blood present in urine (BPU).
The output fields refers to the class/group of tuberculosis disease in the patient. This system uses
Mamdani inference method. The results obtained from designed system are compared with the data in
the database and observed results of designed system are correct. The system was designed with Java
(Jfuzzylogic), Microsoft visio (2013), mySql workbench, MySql database, JSP and XHML.
This document discusses using data mining techniques like association rule mining and improved apriori algorithm with fuzzy logic to develop an expert system that can predict the risk of osteoporosis based on a patient's clinical data and history. It aims to help doctors make more informed decisions early on to prevent osteoporosis. The system would find relationships between various risk factors and diagnose osteoporosis severity to identify at-risk patients before costly tests. Literature on using different algorithms like decision trees and neural networks for medical diagnosis and predicting osteoporosis risk is also reviewed.
Lung Nodule Feature Extraction and Classification using Improved Neural Netwo...IRJET Journal
1) The document presents a technique for lung nodule feature extraction and classification using an Improved Neural Network Algorithm (INNA).
2) Texture features are extracted from CT lung images containing nodules using a Grey Level Co-occurrence Matrix based gradient approach.
3) The extracted features are used to classify lung nodules using INNA, which utilizes an enhanced backpropagation learning rule.
4) Simulation results show the proposed INNA technique achieves 98.99% accuracy in classifying cancer datasets, outperforming other techniques.
Automated Extraction Of Reported Statistical Analyses Towards A Logical Repr...Nat Rice
This document describes research on developing an automated system to extract key information from clinical trial literature, such as the hypothesis, sample size, statistical tests used, and conclusions. The system maps extracted phrases to relevant knowledge sources. It was trained and tested on 42 full-text articles about chemotherapy for non-small cell lung cancer, achieving a precision of 86%, recall of 78%, and F-score of 0.82 for classifying sentences. The goal is to utilize this extracted information for quality assessment, meta-analysis, and disease modeling.
Breast cancer diagnosis via data mining performance analysis of seven differe...cseij
According to World Health Organization (WHO), breast cancer is the top cancer in women both in the
developed and the developing world. Increased life expectancy, urbanization and adoption of western
lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are
diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer
outcome and survival is very crucial.
In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast
cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose
cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
the other patients are realized through seven different algorithms and the accuracies of those have been
given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr.
William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process,
RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.
Machine learning approach for predicting heart and diabetes diseases using da...IAESIJAI
This document describes a study that uses machine learning techniques to predict heart disease and diabetes from medical data. The study collected data from a public repository and preprocessed it to handle missing values. Feature selection was performed using chi-square and principal component analysis to identify important features. Three boosting classifiers - Adaptive boosting, Gradient boosting, and Extreme Gradient boosting - were trained on the data and evaluated based on accuracy. The results showed that the boosting classifiers achieved accurate prediction for both heart disease and diabetes, with the highest accuracy reported for specific classifiers and diseases.
Performance Evaluation of Data Mining Algorithm on Electronic Health Record o...BRNSSPublicationHubI
This document discusses the performance evaluation of various data mining algorithms on an electronic health record database of diabetic patients. It first provides background on data mining and its applications in healthcare, particularly for diabetes. It then describes the methodology used, which involved preprocessing the data and applying several classification algorithms (decision stump, J48, random forest, neural network, Zero R, One R) to predict diabetes status. The results of each algorithm are evaluated based on accuracy, precision, recall, and error rate. Overall, the document aims to compare the performance of these algorithms on an electronic health record database for diabetes prediction.
Machine Learning and the Value of Health TechnologiesCovance
Machine learning can be applied through the development of algorithms that can unravel or "learn" complex associations in large datasets with limited human input. These algorithms are capable of making predictions that go beyond our capabilities as humans and they can process and analyze more possibilities. Machine learning may help us find answers to questions that we didn't even think of in the past, revealing evidence previously hidden among the data. We can use these methods to dig up imperceptible patterns and allow health technologies to be used at the right time and for the right patient population. (A4 Version)
Ht ai 2015 poster 238 - Efficiency of the Artificial Urinary SphincterREBRATSoficial
The document summarizes research on the artificial urinary sphincter (AS) for treating urinary incontinence following prostatectomy. It finds that while AS has success rates of around 79% and patient satisfaction, the evidence is limited as only one randomized controlled trial exists. Systematic reviews call for more studies directly comparing AS to other surgical therapies. Health technology assessments in Brazil did not recommend AS for public insurance due to low evidence, but private insurance does cover it. More high-quality research is needed to determine the effectiveness and costs of AS versus other options.
Similar to A fuzzy inference system for assessment of the severity of the peptic ulcers (20)
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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2. 264 Computer Science & Information Technology (CS & IT)
[3].Currently, focus on plant research has increased all over the world and a large source of
evidence has been collected to show immense potential of medicinal plants used in various
traditional systems [4].
One of the main group of problems in medical science is related to diagnosing diseases based on
different tests on patients. However, the final diagnosis of an expert is associated with difficulties.
This matter led the physicians to apply computer aided detection and diagnosing tools in the
recent decades.
A prime target for such computerized tools is in the domain of cancer diagnosis. Specifically,
where breast cancer is concerned, the treating physician is interested in ascertaining whether the
patient under examination exhibits the symptoms of a benign case, or whether her case is a
malignant one [16].
The uncertainty issues in decisions making and medical diagnosis are related to incompleteness of
medical science. computer aided detection (CAD) tools are presented with the purpose of
facilitating the diagnosis of different diseases and acceleration of the treatment process [18]-[20].
One of the current applications of the CAD systems is to analysis the severity diagnosis of peptic
ulcer presented in [10].
This study is concerned with the severity diagnosis of peptic ulcer and uses fuzzy inference
systems for automatic diagnosis of disease. The required medical knowledge are aided by fuzzy
systems and achieved data are from tested stomachs. This method assorts patients according to
the length of ulcer.
The present study has been undertaken with the aim to assess the peptic ulcer severity using a
fuzzy system.
2. LITERATURE REVIEW
In recent years, a major class of problems in medical science involves the diagnosis of disease,
based upon various tests performed upon the patient. When several tests are involved, the ultimate
diagnosis may be difficult to obtain, even for a medical expert. This has given rise, over the past
few decades, to computerized diagnostic tools, intended to aid the physician in making sense out
of the welter of data [16].
Soft Computing techniques based on the concept of the fuzzy logic or artificial neural networks
for control problems has grown into a popular research area [11]-[13]. The reason is that classical
control theory usually requires a mathematical model for designing controllers. The inaccuracy of
mathematical modeling of plants usually degrades the performance of the controllers, especially
for nonlinear and complex control problems. Fuzzy logic has the ability to express the ambiguity
of human thinking and translate expert knowledge into computable numerical data.
A fuzzy system consists of a set of fuzzy IF-THEN rules that describe the input-output mapping
relationship of the networks. Obviously, it is difficult for human experts to examine all the input-
output data from a complex system to find proper rules for a fuzzy system. To cope with this
difficulty, several approaches that are used to generate the fuzzy IF-THEN rules from numerical
data have been proposed [11]-[13].
Today, medical endoscopy is a widely used procedure to inspect the inner cavities of the human
body. The advent of endoscopic imaging techniques allow the acquisition of images or videos
created the possibility for the development of the whole new branch of computer-aided decision
3. Computer Science & Information Technology (CS & IT) 265
support systems. This section summarizes related works specifically targeted at computer-aided
decision support in the gastrointestinal tract [10].
A symbiotic evolution-based fuzzy-neural diagnostic system for common acute abdominal pain
presents a symbiotic evolution-based fuzzy-neural diagnostic system (SE-FNAAPDS) for
diagnosis of common acute abdominal pain (AAP) without professional medical examination
[11]. The computer-assisted diagnostic system is formatted a multiple-choice symptom
questionnaire, with a prompt/help menu to assist user in obtaining accurate symptom data using
nothing more technologically sophisticated than a medical-type thermometer and stethoscope.
Compared to traditional methods, diagnostic decisions from SE-FNAAPDS shows 94%
agreement with professional human medical diagnosis and less CPU time for system construction.
The presented method is useful as a core module for more advanced computer-assisted diagnostic
systems, and for direct application in AAP diagnosis [11].
Non-ulcer dyspepsia (NUD) has been attributed to gastritis and Helicobacter infection in A
Quantitative analysis of symptoms of Non-Ulcer Dyspepsia as related to age, pathology, and
Helicobacter Infection. The Sydney classification enables dyspepsia symptoms assessed
quantitatively in relation to Helicobacter infection and topographic pathology in different gastric
compartments. The method presented in this study for 348 patients with the NUD. It studied the
unconfounded effects of age, pathology, and Helicobacter. It was concluded that age was the most
important determinant of dyspeptic symptoms, but not pathology or Helicobacter [7].
Computer-aided capsule endoscopy images evaluation based on color. Rotation and texture
features were used as an educational tool to physicians.
Wireless capsule endoscopy (WCE) is a revolutionary, patient-friendly imaging technique that
enables non-invasive visual inspection of the patient’s digestive tract and, especially, small
intestine. Experimental results demonstrated promising classification accuracy (91.1%) exhibiting
high potential towards a complete computer-aided diagnosis system that will not only reduce the
Wireless capsule endoscopy (WCE) data reviewing time, but also serve as an assisting tool for the
training of inexperienced physicians [9].
3. MATERIALS AND METHODS
Fuzzy set A in universe of discourse X can be defined as a set of ordered pairs of element x in X
and the grade of membership of x, µA (x), to fuzzy set A [15] as follows:
where the two dimensional membership function µA (x) is a crisp value between 0 and 1 for all
x∈X. Linguistic terms are modelled using fuzzy sets. One of the parameters in the design of a
fuzzy logic is the number of fuzzy sets associated to a linguistic term. Fuzzy inference system as a
soft computing method mimics cognitive reasoning of the human mind based on linguistic terms
for performing tasks in a natural environments.
Figure 1. Architecture of a Fuzzy Inference System
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The fuzzy inference system is a rule-based system that uses fuzzy logic, rather than Boolean
logic, to reason about data. Its basic structure includes four main components, as depicted in
Figure 1: (1) a fuzzifier, which translates crisp (real-valued) inputs into fuzzy values; (2) an
inference engine that applies a fuzzy reasoning mechanism to obtain a fuzzy output; (3) a
defuzzifier, which translates this latter output into a crisp value; and (4) a knowledge base, which
contains both an ensemble of fuzzy rules, known as the rule base, and an ensemble of
membership functions, known as the database[16].
The fuzzy inference system is a popular computing framework based on the concepts of fuzzy set
theory, fuzzy if-then rules, and fuzzy reasoning. It has found successful applications in a wide
variety of field, such as automatic control, data classification, decision analysis, expert systems,
and pattern recognition.
This Mapping is accomplished by a number of fuzzy if-then rules, each of which describes the
local behavior of the mapping. In particular, the antecedent of a rule defines a fuzzy region in the
input space, while the consequent specifies the output in the fuzzy region.
Fuzzy logic models can be developed from expert knowledge or from process (patient) input-
output data. In the first case, fuzzy models can be extracted from the expert knowledge of the
process. The expert knowledge can be expressed in terms of linguistics, which is sometimes
faulty and requires the model to be tuned. This process requires defining the model input
variables and the determination of the fuzzy model system parameters.
Sugeno, or Takagi-Sugeno-Kang, method of fuzzy inference. Introduced in 1985, it is similar to
the Mamdani method in many respects. The first two parts of the fuzzy inference process,
fuzzifying the inputs and applying the fuzzy operator, are exactly the same. The main difference
between Mamdani and Sugeno is that the Sugeno output membership functions are either linear
or constant [21]. This study applies the Sugeno fuzzy inference model in order to present a
measure of the sevirity of the peptic ulcer in the output of the FIS.
4. PEPTIC ULCER SPECIFICATION
This section explains the chemical process and animals used in laboratory experiments. The
features extracted in the experiments were considered as the input of the FIS. These features are
explained in details in this section.
The present study was tested on male Wistar rats for 15 days protected the gastric mucosa against
the damage induced by indomethacin (25, 50 and 100 mg/kg) [17]. Male Wistar rats weighing
175 - 220 g were used in the study. The animals were in 6 separate groups consisting of 5 rats.
The quantitative evaluation of experimentally induced gastric lesions is a problematic and error-
prone task due to their predominantly multiple and irregularly shaped occurrence. The simplest
type of lesion index for quantification of chemically induced ulcers were described as the
cumulative length (mm) of all hemorrhagic erosions. The width of lesion has also been taken into
account (ulcer index = length -width) [17]. Figure 2 shows Microscope views of a sample
stomach of a rat with ulcer.
Figure 2. Microscope views of the rats stomach with ulcer
5. Computer Science & Information Technology (CS & IT) 267
5. FUZZY MODELING OF THE FEATURE CHARACTERIZATION OF
PEPTIC ULCER
In order to apply soft computing techniques based on the FIS for severity assessment of the peptic
ulcer, we applied two methods as follows:
1) FCM (Fuzzy C-Means Clustering): for scatter partitioning of the input space and
automatic generation of the membership functions
2) ANFIS (Adaptive Neuro-Fuzzy inference system) for learning the FIS rules and tuning of
the membership functions
The rest of this section explains the abovementioned processes in details.
5.1. Scatter partitioning of the input space
Fuzzy c-means (FCM) is a data clustering technique wherein each data point belongs to a cluster
to some degree that is specified by a membership grade. This technique was originally introduced
by Jim Bezdek in 1981 as an improvement on earlier clustering methods. It provides a method
that shows how to group data points that populate some multidimensional space into a specific
number of different clusters.
In this study we used Fuzzy Logic Toolbox in Matlab to implement the FIS. The FCM starts with
an initial guess for the cluster centers, which are intended to mark the mean location of each
cluster. The initial guess for these cluster centers is most likely incorrect. Additionally, fcm
assigns every data point a membership grade for each cluster. By iteratively updating the cluster
centers and the membership grades for each data point, FCM iteratively moves the cluster centers
to the right location within a data set. This iteration is based on minimizing an objective function
that represents the distance from any given data point to a cluster center weighted by that data
point's membership grade Input features for assessment of the peptic ulcer are as Follows:
For each input and output variable of the FIS, three linguistic terms (Low, Medium and High)
were considered. Table 1 shows all input variables of the peptic ulcer FIS.
Table 1. The FIS input variables
No. Feature Description
1 Score 1 Each fifth petechia was calculated as 1 mm
2 Score 2 lesion length between 1 and 2 mm
3 Score 3 lesion length between 2 and 4 mm
4 Score 4 lesion length between 4 and 6 mm
5 Score 5 lesion length more than 6 mm
6 Indomethacin Explained in Section IV
7 Cimitidine Explained in Section IV
The FIS output variable were considered ulcer index (UI) which represents severity of the peptic
ulcer. The ulcer index (UI) was calculated using the following formula:
UI =( 1*S1)+(2*S2)+(3*S3)+(4*S4)+(5*S5) (1)
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where S1, S2, S3, S4, S5 are related to the score 1 to score 5, respectively.
5.2. ANFIS (Adaptive Neuro-Fuzzy Inference System)
This syntax is the major training routine for Sugeno -type fuzzy inference systems. anfis uses a
hybrid learning algorithm to identify parameters of Sugeno-type fuzzy inference systems. It
applies a combination of the least-squares method and the backpropagation gradient descent
method for training FIS membership function parameters to emulate a given training data set.
ANFIS can also be invoked using an optional argument for model validation. We applied the
ANFIS for learning rules in the FIS and tuning of the membership function parameters.
The flowchart of the approach applied for learning and tuning of the FIS parameters using the
ANFIS approach is shown in Figure 3.
6. EXPERIMENTS RESULTS
In the process of the FIS parameter specification using the ANFIS model, we have a dataset
including 30 real patients diagnosed with peptic ulcer information. We partitioned the dataset into
two parts:
1) Training (70%)
2) Testing (30%)
Figures 4 to 9 represent the performance results on training and testing datasets in terms of the
root mean square error (RMSE) and the histogram of the errors. The performance results are
summarized in Table 2.
Table 2. System Performance on Train and Test datasets
Accuracy
Average(train) 99.65%
Average(test) 97.74%
The rules and membership functions of the FIS for peptic ulcer risk assessment was designed
using fuzzy c-means (FCM) clustering by extracting a set of rules that models the data behaviour
using Fuzzy Logic Toolbox and ANFIS Toolbox in Matlab are shown in Figures 9 to 14. The rule
extraction method first uses the FCM method to determine the number of rules and membership
functions for the antecedents and consequents. Then ANFIS is applied to tune the FIS parameters.
Table 3 shows the resulted FIS before training and after training process using the ANFIS
approach. The RMSE was used as performance measure during evaluation process. The result of
the RMSE and the histogram of the errors on train and test datasets are shown in Figures 3 to 8.
Table 3. Comparison of Results
Methods Accuracy
FCM 94.9%
ANFIS 98.1%
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Figure 3. This section shows the Train data. Figure 4. RMSE: Shows the maximum and
Almost coincides target and Output. minimum errors in the Train Data.
Figure 5. The third part is the histogram of Figure 6. This section shows the Test data.
the errors,and shows the mean and standard There is between Output and Target.
deviation of the error.
Figure 7. RMSE: Shows the maximum and Figure 8. The third part is the histogram of
minimum errors in the Test Data. the errors, and shows the mean and
standard deviation of the error.
Figure 9. Membership functions related to Figure 10. Membership functions related to
the Score 3: lesion length between 2 and the Score 4: lesion length between 4 and 6 mm
4 mm
7. CONCLUSION
In this study, for the first time, a soft computing technique based upon fuzzy inference system
(FIS) was proposed for the problem of peptic ulcer assessment. The FIS was generated using
FCM and tuned using the ANFIS model. Performance results on a dataset including real patients
reveal the FIS with maximum accuracy of 98.1%, which reveals superiority of the approach. The
8. 270 Computer Science & Information Technology (CS & IT)
intelligent FIS system can help medical experts as a second reader for detection of the peptic ulcer
in the decision making process and consequently, improves the treatment process.
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AUTHORS
Kianaz Rezaei (born February, 1990), obtained her B.Sc. degree in Computers-Soft
computing from Islamic Azad University, Share-e-Qods Branch, Tehran, Iran during 2012,
and currently pursuing Masters Degree from Kharazmi University, Tehran, Iran.
Rahil Hosseini is assistant professor in software engineering and soft computing at Azad
University. She is a member of the Digital Imaging Research Centre (DIRC) in the Faculty
of Science, Engineering and Computing at Kingston University, London. She received her
BSc at Teacher training University in Tehran followed by her MSc in software engineering.
She commenced her PhD at Kingston University London. Her main area of interest in
research is in the fields of soft computing, fuzzy modelling and pattern recognition in data
mining problems and medical image analysis, specifically cancer diagnosis. She has professional work
experience in the field of data mining, medical image analysis and fuzzy modelling for uncertain
environments. She has published journal and conference papers in data mining, soft computing, distributed
systems, fuzzy modelling, and pattern recognition for medical image analysis and cancer diagnosis.
Mahdi Mazinani is assistant professor in electronic engineering and image processing at
Azad University. He is a member of the Quantitative Medical Imaging International
Institutes (QMI3) and Digital Imaging Research Centre in the Faculty of Science,
Engineering and Computing at Kingston University, London. After completing his BSc
degree in Electronic engineering at Semnan University (securing the first rank) he
completed his MSc degree in the Semnan University with first rank. He commenced his
PhD. His main area of interest in research is in the field of medical image analysis. His work is focused on
research into novel image analysis techniques to enable the recognition and quantification using CTA
images.
He has published papers in the detection and quantification of the coronary arteries using fuzzy and medical
image analysis techniques.