Chest diseases if not properly diagnosed in early stages can be fatal. Because of lack of skilled
knowledge or experiences of real life practitioners, many a times one chest disease is wrongly diagnosed for the
other, which leads to wrong treatment. Due to this the actual disease keeps on growing and become fatal. For
example, muscular chest pains can be treated for the heart disease or COPD is treated for Asthma. Early
prediction of chest disease is crucial but is not an easy task. Consequently, the computer based prediction system
for chest disease may play a significant role as a pre-stage detection to take proper actions with a view to recover
from it. However the choice of the proper Data Mining classification method can effectively predict the early
stage of the disease for being cured from it. In this paper, the three mostly used classification techniques such as
support vector machine (SVM), k-nearest neighbour (KNN) and artificial neural network (ANN) have been studied
with a view to evaluating them for chest disease prediction.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical
research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more
number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well
as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
Performance evaluation of random forest with feature selection methods in pre...IJECEIAES
Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes.
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical
research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more
number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well
as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
Performance evaluation of random forest with feature selection methods in pre...IJECEIAES
Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes.
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
(Paper) Emergency Medical Support System for Visualizing Locations and Vital ...Naoki Shibata
The triage tag is used in Mass Casualty Incident (MCI) to check the priority of patients treatments and conditions. However, it is difficult to grasp a change in the patient’s information since it is a paper tag. In this paper, we propose a system using the electronic triage tag (eTriage) that facilitates emergency medical technicians to grasp patients locations and conditions through visualization. This system provides the following three views of the patients information: (1) Inter-site view which shows on a map an overview of the latest status in multiple first-aid stations including the number of technicians and patients of each triage category; (2) Intra-site view which shows detailed status of each first-aid station including the location, triage category, and vital signs of each patient on a 3D map created based on the environment mapping technique; and (3) Individual view which shows vital information of patients on a tablet PC according to its orientation using the augmented reality technique. In this paper, we describe the design and implementation of the proposed system with some preliminary evaluation results.
Various Data Mining Techniques for Diabetes Prognosis: A Reviewijtsrd
Most of the food we eat is converted to glucose, or sugar which is used for energy. When you have diabetes, your body either doesnt make enough insulin or cannot use its own insulin as well as it should. This causes sugar to build up in your blood leading to complications like heart disease, stroke, neuropathy, poor circulation leading to loss of limbs, blindness, kidney failure, nerve damage, and death. Data mining adopts a series of pattern recognition technologies and statistical and mathematical techniques to discover the possible rules or relationships that govern the data in the databases. Data mining plays an important role in data prediction. There are different types of diseases predicted in data mining namely Hepatitis, Lung Cancer, Liver disorder, Breast cancer, Thyroid disease, Diabetes etc¦ This paper analyzes the Diabetes predictions. Misba Reyaz | Gagan Dhawan"Various Data Mining Techniques for Diabetes Prognosis: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12927.pdf http://www.ijtsrd.com/engineering/computer-engineering/12927/various-data-mining-techniques-for-diabetes-prognosis-a-review/misba-reyaz
Presented at the Master of Science Program in Medical Epidemiology and the Doctor of Philosophy Program in Clinical Epidemiology, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 25, 2021
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
In this research work we have developed a strategy in which the various parameters that influence the occurrence of pulmonary disease have been gathered from survey of doctors who specialize in diagnoses of pulmonary disease and diagnostic recipes involving if the else rules were built and given labels, which were used as target for machine learning algorithms [Logistic , SVM, RBF, Naïve Bayes ] for identification of input dataset of symptoms of subjects . Multiple designs of these classifiers were implemented and best possible machine algorithm was identified for implementing the complete methodology. Results shows that there was no absolute answer for the design and selection of best possible machine algorithm as evident from the results based on multiple statistical tests, therefore , distance from ideal values of statistical test to find best classifier with most optimized parameters was calculated and the classifier which had closest to these ideal values was found and declared the best classifier for identification of pulmonary diseases presence or absence .as per results naïve bayes classifier is performing best which is evident from the statistical test scores .
(Paper) Emergency Medical Support System for Visualizing Locations and Vital ...Naoki Shibata
The triage tag is used in Mass Casualty Incident (MCI) to check the priority of patients treatments and conditions. However, it is difficult to grasp a change in the patient’s information since it is a paper tag. In this paper, we propose a system using the electronic triage tag (eTriage) that facilitates emergency medical technicians to grasp patients locations and conditions through visualization. This system provides the following three views of the patients information: (1) Inter-site view which shows on a map an overview of the latest status in multiple first-aid stations including the number of technicians and patients of each triage category; (2) Intra-site view which shows detailed status of each first-aid station including the location, triage category, and vital signs of each patient on a 3D map created based on the environment mapping technique; and (3) Individual view which shows vital information of patients on a tablet PC according to its orientation using the augmented reality technique. In this paper, we describe the design and implementation of the proposed system with some preliminary evaluation results.
Various Data Mining Techniques for Diabetes Prognosis: A Reviewijtsrd
Most of the food we eat is converted to glucose, or sugar which is used for energy. When you have diabetes, your body either doesnt make enough insulin or cannot use its own insulin as well as it should. This causes sugar to build up in your blood leading to complications like heart disease, stroke, neuropathy, poor circulation leading to loss of limbs, blindness, kidney failure, nerve damage, and death. Data mining adopts a series of pattern recognition technologies and statistical and mathematical techniques to discover the possible rules or relationships that govern the data in the databases. Data mining plays an important role in data prediction. There are different types of diseases predicted in data mining namely Hepatitis, Lung Cancer, Liver disorder, Breast cancer, Thyroid disease, Diabetes etc¦ This paper analyzes the Diabetes predictions. Misba Reyaz | Gagan Dhawan"Various Data Mining Techniques for Diabetes Prognosis: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12927.pdf http://www.ijtsrd.com/engineering/computer-engineering/12927/various-data-mining-techniques-for-diabetes-prognosis-a-review/misba-reyaz
Presented at the Master of Science Program in Medical Epidemiology and the Doctor of Philosophy Program in Clinical Epidemiology, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 25, 2021
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
In this research work we have developed a strategy in which the various parameters that influence the occurrence of pulmonary disease have been gathered from survey of doctors who specialize in diagnoses of pulmonary disease and diagnostic recipes involving if the else rules were built and given labels, which were used as target for machine learning algorithms [Logistic , SVM, RBF, Naïve Bayes ] for identification of input dataset of symptoms of subjects . Multiple designs of these classifiers were implemented and best possible machine algorithm was identified for implementing the complete methodology. Results shows that there was no absolute answer for the design and selection of best possible machine algorithm as evident from the results based on multiple statistical tests, therefore , distance from ideal values of statistical test to find best classifier with most optimized parameters was calculated and the classifier which had closest to these ideal values was found and declared the best classifier for identification of pulmonary diseases presence or absence .as per results naïve bayes classifier is performing best which is evident from the statistical test scores .
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
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 health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure 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. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
AN IMPROVED MODEL FOR CLINICAL DECISION SUPPORT SYSTEMijaia
Misguided information in health care has caused much havoc that have led to the death of millions of people as a result of misclassification, and inconsistent health care records; hence the objective of this paper is to develop an improved clinical decision support system. This system incorporated hybrid system
of non-knowledge based and knowledge based decision support system for the diagnosis of diseases and proper health care delivery records using prostate cancer and diabetes datasets to train and validate the model. The min-max method was adopted in normalizing the datasets, while genetic algorithm was
deployed in initiating the training weights of the MLP. The result obtained in this paper yielded a classification accuracy of 98%, sensitivity of 0.98 and specificity of 100 for prostate cancer and accuracy of 94%, sensitivity of 0.94 and specificity of 0.67 for diabetes.
Plug In Generator To Produce Variant Outputs For Unique Data.IJRES Journal
Our modern world comprising of abundant chronic diseases which are affecting humankind, awful thing is that they affect the people without being notified until the end. In this project we proposed a system in which the user identifies the disease by providing the symptoms which he is experiencing. The user selects the multiple symptoms which he/she is suffering and submits them for evaluation using String Matching System. The database consists of limited number of diseases, with well organized pattern structure of symptoms. Using a friendly interface, user can input the data in the questionnaire form developed. Artificial Bee Colony Optimization [ABC] algorithm, i.e., a Machine Learning algorithm embedded in the project provides an optimistic disease along with its prevention and curing methods, but before ABC produces optimistic disease, String Matching System approach gives an accurate disease with which the human is suffering from. The above said data transformed into web can be considered as an offline browsing system which can be used by any educated personalities, to generally know what is happening and gets enough idea before visiting the practitioner.
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Similar to Proposed Model for Chest Disease Prediction using Data Analytics (20)
Understanding the Impact and Challenges of Corona Crisis on Education Sector...vivatechijri
n the second week of March 2020, governments of all states in a country suddenly declared
shutting down of all colleges and schools for a temporary period of time as an immediate measure to stop the
spread of pandemic that is of novel corona virus. As the days pass by almost close to a month with no certainty
when they will again reopen. Due to pandemic like this an alarm bells have started sounding in the field of
education where a huge impact can be seen on teaching and learning process as well as on the entire education
sector in turn. The pandemic disruption like this is actually gave time to educators of today to really think about
the sector. Through the present research article, the author is highlighting on the possible impact of
coronavirus on education sector with the future challenges for education sector with possible suggestions.
LEADERSHIP ONLY CAN LEAD THE ORGANIZATION TOWARDS IMPROVEMENT AND DEVELOPMENT vivatechijri
This paper is explaining that how only leadership is responsible for sustainable improvement and
growth and only it can lead the organization towards improvement and overall development. Leadership and its
effectiveness are discussed in this research work and also how leadership is a different way of the success of the
organization and different from the traditional management to create true work-culture and good-will of the
organization in the social scene. Leadership is only responsible in bringing positive and negative change in the
organization; if the leadership doesn’t have the concern in the organization, the organization will not be able to
lead in the right direction towards improvement and development.
The topic of assignment is a critical problem in mathematics and is further explored in the real
physical world. We try to implement a replacement method during this paper to solve assignment problems with
algorithm and solution steps. By using new method and computing by existing two methods, we analyse a
numerical example, also we compare the optimal solutions between this new method and two current methods. A
standardized technique, simple to use to solve assignment problems, may be the proposed method
Structural and Morphological Studies of Nano Composite Polymer Gel Electroly...vivatechijri
n today’s society, we stand before a change in energy scarcity. As our civilization grows, many
countries in thedeveloping world seek to have the standard of living that has been exclusive to a few nations, so
their arises a need in thedevelopment of technology that is compatible enough with the resources provided by
nature in order to have sustainabledevelopment to all class of the society. In order to overcomethe prevailing
challenges of huge energy crises in near future, there is an urgent need for the development of electrical
vehiclesor hybrid electrical vehicles with low CO2 emissions using renewable energy sources. In view of the
above, electrochemicalcapacitors can fulfil the requirements to some extent.Preparation of nano composite
polymer gel electrolyte is the best optional product to overcome these problems. When fillers are added or
dispersed to the polymer gel electrolyte, amorphous or porous nature of electrolyte increases which enhances
the liquid absorbing quality of polymer and helps in removing the drawbacks of polymer gel electrolytes such as
leakage, poor mechanical and thermal stability etc. In this work dispersion of SiO2 nano filler is done in the
[PVdF (HFP)-PC-Mg (ClO4)2] for the synthesis of nano composite PGE [PVdF (HFP)-PC-MgClO4- SiO2].
Optimization and characterization was carried out by using various techniques.
Theoretical study of two dimensional Nano sheet for gas sensing applicationvivatechijri
This study is focus on various two dimensional material for sensing various gases with theoretical
view for new research in gas sensing application. In this paper we review various two dimensional sheet such as
Graphene, Boron Nitride nanosheet, Mxene and their application in sensing various gases present in the
atmosphere.
METHODS FOR DETECTION OF COMMON ADULTERANTS IN FOODvivatechijri
Food is essential forliving. Food adulteration deceives consumers and can endanger their health. The
purpose of this document is to list common food adulterant methods commonly found in India. An adulterant is
a substance found in other substances such as food, cosmetics, pharmaceuticals, fuels, or other chemicals that
compromise the safety or effectiveness of that substance. The addition of adulterants is called adulteration. The
most common reason for adulteration is the use of undeclared materials by manufacturers that are cheaper than
the correct and declared ones. The adulterants can be harmful or reduce the effectiveness of the product, or
they can be harmless.
The novel ideas of being a entrepreneur is a key for everyone to get in the hustle, but developing a
idea from core requires a systematic plan, time management, time investment and most importantly client
attention. The Time required for developing may vary from idea to idea and strength of the team. Leadership to
build a team and manage the same throughout the peak of development is the main quality. Innovations and
Techniques to qualify the huddles is another aspect of Business Development and client Retention.
Innovation for supporting prosperity has for quite some time been a focus on numerous orders, including PC science, brain research, and human-PC connection. In any case, the meaning of prosperity isn't continuously clear and this has suggestions for how we plan for and evaluate advances that intend to cultivate it. Here, we talk about current meanings of prosperity and how it relates with and now and then is a result of self-amazing quality. We at that point center around how innovations can uphold prosperity through encounters of self-amazing quality, finishing with conceivable future bearings.
An Alternative to Hard Drives in the Coming Future:DNA-BASED DATA STORAGEvivatechijri
Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up, there emerges a requirement for a storage medium with high capacity, high storage density, and possibility to face up to extreme environmental conditions. According to a research in 2018, every minute Google conducted 3.88 million searches, other people posted 49,000 photos on Instagram, sent 159,362,760 e-mails, tweeted 473,000 times and watched 4.33 million videos on YouTube. In 2020 it estimated a creation of 1.7 megabytes of knowledge per second per person globally, which translates to about 418 zettabytes during a single year. The magnetic or optical data-storage systems that currently hold this volume of 0s and 1s typically cannot last for quite a century. Running data centres takes vast amounts of energy. In short, we are close to have a substantial data-storage problem which will only become more severe over time. Deoxyribonucleic acid (DNA) are often potentially used for these purposes because it isn't much different from the traditional method utilized in a computer. DNA’s information density is notable, 215 petabytes or 215 million gigabytes of data can be stored in just one gram of DNA. First we can encode all data at a molecular level and then store it in a medium that will last for a while and not become out-dated just like floppy disks. Due to the improved techniques for reading and writing DNA, a rapid increase is observed in the amount of possible data storage in DNA.
The usage of chatbots has increased tremendously since past few years. A conversational interface is an interface that the user can interact with by means of a conversation. The conversation can occur by speech but also by text input. When a chatty interface uses text, it is also described as a chatbot or a conversational medium. During this study, the user experience factors of these so called chatbots were investigated. The prime objective is “to spot the state of the art in chatbot usability and applied human-computer interaction methodologies, to research the way to assess chatbots usability". Two sorts of chatbots are formulated, one with and one without personalisation factors. the planning of this research may be a two-by-two factorial design. The independent variables are the two chatbots (unpersonalised versus personalised) and thus the speci?c task or goal the user are ready to do with the chatbot within the ?nancial ?eld (a simple versus a posh task). The results are that there was no noteworthy interaction effect between personalisation and task on the user experience of chatbots. A signi?cant di?erence was found between the two tasks with regard to the user experience of chatbots, however this variation wasn't because of personalisation.
The Smart glasses Technology of wearable computing aims to identify the computing devices into today’s world.(SGT) are wearable Computer glasses that is used to add the information alongside or what the wearer sees. They are also able to change their optical properties at runtime.(SGT) is used to be one of the modern computing devices that amalgamate the humans and machines with the help of information and communication technology. Smart glasses is mainly made up of an optical head-mounted display or embedded wireless glasses with transparent heads- up display or augmented reality (AR) overlay in it. In recent years, it is been used in the medical and gaming applications, and also in the education sector. This report basically focuses on smart glasses, one of the categories of wearable computing which is very popular presently in the media and expected to be a big market in the next coming years. It Evaluate the differences from smart glasses to other smart devices. It introduces many possible different applications from the different companies for the different types of audience and gives an overview of the different smart glasses which are available presently and will be available after the next few years.
Future Applications of Smart Iot Devicesvivatechijri
With the Internet of Things (IoT) bit by bit creating as the resulting time of the headway of the Internet, it gets critical to see the diverse expected zones for the utilization of IoT and the research challenges that are connected with these applications going from splendid savvy urban areas, to medical care administrations, shrewd farming, collaborations and retail. IoT is needed to attack into for all expectations and purposes for all pieces of our day-to-day life. Despite the fact that the current IoT enabling advancements have immensely improved in the continuous years, there are so far different issues that require attention. Since the IoT ideas results from heterogeneous advancements, many examination difficulties will arise. In like manner, IoT is planning for new components of exploration to be finished. This paper presents the progressing headway of IoT advancements and inspects future applications.
Cross Platform Development Using Fluttervivatechijri
Today the development of cross-platform mobile application has under the state of compromise. The developers are not willing to choose an alternative of either building the similar app many times for many operating systems or to accept a lowest common denominator and optimal solution that will going to trade the native speed, accuracy for portability. The Flutter is an open-source SDK for creating high-performance, high fidelity mobile apps for the development of iOS and Android. Few significant features of flutter are - Just-in-time compilation (JIT), Ahead- of-time compilation (AOT compilation) into a native (system-dependent) machine code so that the resulting binary file can execute natively. The Flutter’s hot reload functionality helps us to understand quickly and easily experiment, build UIs, add features, and fix bugs. Hot reload works by injecting updated source code files into the running Dart Virtual Machine (VM). With the help of Flutter, we believe that we would be having a solution that gives us the best of both worlds: hardware accelerated graphics and UI, powered by native ARM code, targeting both popular mobile operating systems.
The Internet, today, has become an important part of our lives. The World Wide Web that was once a small and inaccessible data storage service is now large and valuable. Current activities partially or completely integrated into the physical world can be made to a higher standard. All activities related to our daily life are mapped and linked to another business in the digital world. The world has seen great strides in the Internet and in 3D stereoscopic displays. The time has come to unite the two to bring a new level of experience to the users. 3D Internet is a concept that is yet to be used and requires browsers to be equipped with in-depth visualization and artificial intelligence. When this material is included, the Internet concept of material may become a reality discussed in this paper. In this paper we have discussed the features, possible setting methods, applications, and advantages and disadvantages of using the Internet. With this paper we aim to provide a clear view of 3D Internet and the potential benefits associated with this obviously cost the amount of investment needed to be used.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
The study LiFi (Light Fidelity) demonstrates about how can we use this technology as a medium of communication similar to Wifi . This is the latest technology proposed by Harold Haas in 2011. It explains about the process of transmitting data with the help of illumination of an Led bulb and about its speed intensity to transmit data. Basically in this paper, author will discuss about the technology and also explain that how we can replace from WiFi to LiFi . WiFi generally used for wireless coverage within the buildings while LiFi is capable for high intensity wireless data coverage in limited areas with no obstacles .This research paper represents introduction of the Lifi technology,performance,modulation and challenges. This research paper can be used as a reference and knowledge to develop some of LiFitechnology.
Social media platform and Our right to privacyvivatechijri
The advancement of Information Technology has hastened the ability to disseminate information across the globe. In particular, the recent trends in ‘Social Networking’ have led to a spark in personally sensitive information being published on the World Wide Web. While such socially active websites are creative tools for expressing one’s personality it also entails serious privacy concerns. Thus, Social Networking websites could be termed a double edged sword. It is important for the law to keep abreast of these developments in technology. The purpose of this paper is to demonstrate the limits of extending existing laws to battle privacy intrusions in the Internet especially in the context of social networking. It is suggested that privacy specific legislation is the most appropriate means of protecting online privacy. In doing so it is important to maintain a balance between the competing right of expression, the failure of which may hinder the reaping of benefits offered by Internet technology
THE USABILITY METRICS FOR USER EXPERIENCEvivatechijri
THE USABILITY METRICS FOR USER EXPERIENCE was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as THE USABILITY METRICS FOR USER EXPERIENCE that is GFS. THE USABILITY METRICS FOR USER EXPERIENCE is one of the largest file system in operation. Generally THE USABILITY METRICS FOR USER EXPERIENCE is a scalable distributed file system of large distributed data intensive apps. In the design phase of THE USABILITY METRICS FOR USER EXPERIENCE, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. THE USABILITY METRICS FOR USER EXPERIENCE also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, THE USABILITY METRICS FOR USER EXPERIENCE is highly available, replicas of chunk servers and master exists.
Google File System was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as Google File System that is GFS. Google File system is one of the largest file system in operation. Generally Google File System is a scalable distributed file system of large distributed data intensive apps. In the design phase of Google file system, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. Google File System also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, Google file system is highly available, replicas of chunk servers and master exists.
A Study of Tokenization of Real Estate Using Blockchain Technologyvivatechijri
Real estate is by far one of the most trusted investments that people have preferred, being a lucrative investment it provides a steady source of income in the form of lease and rents. Although there are numerous advantages, one of the key downsides of real estate investments is lack of liquidity. Thus, even though global real estate investments amount to about twice the size of investments in stock markets, the number of investors in the real estate market is significantly lower. Block chain technology has real potential in addressing the issues of liquidity and transparency, opening the market to even retail investors. Owing to the functionality and flexibility of creating Security Tokens, which are backed by real-world assets, real estate can be made liquid with the help of Special Purpose Vehicles. Tokens of ERC 777 standard, which represent fractional ownership of the real estate can be purchased by an investor and these tokens can also be listed on secondary exchanges. The robustness of Smart Contracts can enable the efficient transfer of tokens and seamless distribution of earnings amongst the investors. This work describes Ethereum blockchainbased solutions to make the existing Real Estate investment system much more efficient.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Top 10 Oil and Gas Projects in Saudi Arabia 2024.pdf
Proposed Model for Chest Disease Prediction using Data Analytics
1. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 2 (2019)
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Proposed Model for Chest Disease Prediction using Data
Analytics
Vikrant A. Agaskar 1
and Umesh Kulkarni2
1
(PG Student ARMIET, Dist. Thane, India)
2
(Vidyalankar Institute of Technology, India)
Abstract: Chest diseases if not properly diagnosed in early stages can be fatal. Because of lack of skilled
knowledge or experiences of real life practitioners, many a times one chest disease is wrongly diagnosed for the
other, which leads to wrong treatment. Due to this the actual disease keeps on growing and become fatal. For
example, muscular chest pains can be treated for the heart disease or COPD is treated for Asthma. Early
prediction of chest disease is crucial but is not an easy task. Consequently, the computer based prediction system
for chest disease may play a significant role as a pre-stage detection to take proper actions with a view to recover
from it. However the choice of the proper Data Mining classification method can effectively predict the early
stage of the disease for being cured from it. In this paper, the three mostly used classification techniques such as
support vector machine (SVM), k-nearest neighbour (KNN) and artificial neural network (ANN) have been studied
with a view to evaluating them for chest disease prediction.
Keywords – KNN, SVM, Data Analysis, ANN.
1. INTRODUCTION
Human beings suffer from a wide variety of chest-related diseases. These chest diseases include asthma,
copd, pneumonia, tuberculosis, etc. The chest diseases have symptoms that demonstrate their presence. Symptoms
include shortness of breath, chest congestion, chest pain, cough from the throat, and cough from the chest, etc.
and manifest in difference, these are the common symptoms which are found in many situations. When human
beings do regular functions in their day to day lives, they are prone to seek these symptoms in situations such as
running, walking, long breathing up, etc. To detect which chest disease the human being might be facing, a plan
is identified by which decision can be made by making use of a symptom-based questionnaire. To make the
machine understand and predict which disease the patient suffers from, it must be trained on the sample datasets
containing symptoms in questionnaire. Such datasets can be obtained from UCI database, CHHS (California
health and human services) database, as well as data from reputed national institute of tuberculosis and respiratory
diseases.
A large number of people who suffer from chest related diseases die due to wrong predication of chest
conditions. This is often due to the fact that they are diagnosed much later after the disease occurs, after which it
becomes difficult to solve the problem. In addition to this, they are often misdiagnosed for one another. A patient
with Asthma may be told he has COPD and vice versa since there is a very thin line difference between these two
diseases. Initially they are so identical that hardly difference is there. This leads to the wrong treatment being
given to the patient and causes adverse effects of the treatment. Therefore, there is a need to build an easy system
to aid doctors for preliminary decision making. There is also a need to empower the patient with a tool that helps
him understand his condition better and take appropriate measures by giving proper information of his condition
of health to the correct doctor.
Mainly focus is on collection of information for Knowledge Discovery in Databases (KDD). This is
initial process from which mashup candidates are identified by addressing a repository of open services. Within
this approach, there is a customized approach to life cycle which software engineers can use to generate new
applications based on service integration techniques. KDD also define service integration qualification by
discovering different aspects of web service specifications.
2. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 2 (2019)
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2. AVAILABLE METHODS
a. Support Vector Machine (SVM)
Support vector machine is a supervised learning model that is defined as the finite dimensional vector
spaces where each dimension characterizes a feature of a particular object. In this way, SVM has been proved as
an effective method in high-dimensional space problems. Due to its computational competence on huge datasets
SVM is typically used in document classification, sentiment analysis and prediction-based tasks
b. K-Nearest Neighbors (KNN)
K-Nearest Neighbor (KNN), a supervised learning model as well, is used to classify the test data using
the training samples directly. In KNN, an object is classified by the majority voting of its closest neighbors.
Alternatively, the class of a new sample is predicted based on some distance metrics where the distance metric
can be a simple Euclidean distance. In the working steps, KNN first calculates k (No. of the nearest neighbors).
After that, it finds the distance between the training data and then sorts the distance. Subsequently, a class label
will be assigned to the test data based on the majority voting.
c. Artificial Neural Network (ANN)
The Artificial Neural Network (ANN), also a supervised learning strategy, contains three layers: input,
hidden and output. The connection between the input units and the hidden and the output units are based on
relevance of the assigned weight of that specific input unit. Usually, if the weight is higher, then it is considered
more important. ANN may use linear and sigmoid transfer (activation) functions. Also, the ANNs are suitable for
the training of large amounts of data with limited inputs. For multi-layer feed forward ANN, the mostly used
learning algorithm is the Backpropagation learning tool. In ANN, the input data records should be separated into
three sub-datasets for the purpose of training, validation and testing.
3. PROPOSED METHOD
Symptom-based Questionnaires required: Asthma, COPD, Pneumonia, Tuberculosis.
Training of machine using datasets:
a. Dataset required for the purpose can be obtained from UCI repository database, CSSH database
b. Datasets from the National Institute of Tuberculosis and Respiratory Diseases (India).
c. An ML training service like Tensor Flow can be used to train the system based on the dataset
selected.
d. A Cloud ML service can be used to verify and double check the training.
e. Predictive analysis is carried out to find the % of accuracy diagnosis for a particular disease
The main steps which are considered when a predicated disease is to be notified.
Step 1. Collection of user data
Step 2. User choice
Step 3. Collection of questionnaire
Step 4. Processing of data collection
Step 5. Comparing of the data received with data set
Step 6. Result of comparison decision to be taken with respected to which chest disease
Step 7. Depending on the decision proceed for treatment, if ok update the data set
Currently systems utilize a large amount of medical data taken from tests that determine the nature of the
chest disease. These are expensive and not scalable in nature and require advanced medical professionals. To
overcome problems on existing system, in proposed system user does not require to search data in various
repository with special features. User need only to give information which is required to be collected. User can
just type combination of queries and based on user behaviour analysis exact data will be predicted.
However, over the years medical researchers have arrived at a synthesis of this medical data to give us
symptom-based questionnaires that can be used by people to detect these diseases. But the limitations of these
questionnaires are that they have been arrived at in small clinical trials using small amounts of patient and control
data.
Therefore, there is a need to build a machine learning system that uses large amounts of patient and
control data to verify and use these symptoms based questionnaires for the broader public. We seek to integrate
several of these symptom-based questionnaires with real life scenario data to e able to precisely and yet easily
predict which chest disease the patient has. There are two kinds of data required, patient data (chest disease
patients and their symptoms) and control data of normal people with no chest conditions. By integrating these
data sets, to create weighted scores for each question in the questionnaire, we will be able to generate a result of
which chest disease the patient is suffering.
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Fig 1. Flow chart.
The main focus of Predictive Diagnosis System will be to implement machine learning algorithms and
Prediction of which chest disease the patient might be suffering from based on the symptoms.
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4. ANALYSIS AND ADVANTAGES
There are important applications for this type of systems there are:
a. In hospitals for doctors to use as an initial diagnosis measure before further check-ups.
b. For self-diagnosis by patients
c. By government and municipal bodies to see the impact of air pollution on health of citizens.
d. Industries to ensure health and welfare of people before setting up manufacturing and other plants
near occupied localities.
Major advantage of the proposed system is that it is generally very difficult to predict chest diseases other
than heart disease as data and specific criteria’s to diagnose chest diseases are not available. All the system which
are available currently are focusing only on the heart disease prediction. Periodic record of PFT (Pulmonary
function test) gives regular input about the patient’s condition. This system can predict COPD and Asthma well
in their initial stages. COPD and Asthma can be controlled if diagnosed in initial stage itself. Whereas Pneumonia
and heart disease can even be diagnosed and treated as substantial research has already been done.
5. CONCLUSION
A prototype chest disease prediction system is developed using three data mining classification Modeling
techniques. The system extracts hidden knowledge from a historical chest disease database. DMX query language
and functions are used to build and access the models. The models are trained and validated against a test dataset.
Lift Chart and Classification Matrix methods are used to evaluate the effectiveness of the models. All three models
are able to extract patterns in response to the predictable state. The most effective model to predict patients with
chest disease appears to be Artificial Neural Network and Decision Trees. The goals are evaluated against the
trained models. All three models could answer complex queries, each with its own strength with respect to ease
of model interpretation, access to detailed information and accuracy. This system can be further enhanced and
expanded. It can also incorporate other data mining techniques, e.g., Time Series, and Association Rules.
Continuous data can also be used instead of just categorical data. Another area is to use Text Mining to mine the
vast amount of unstructured data available in healthcare databases.
REFERENCES
[1] B Shin, SL Cole, S-J Park, DK Ledford, RF Lockey Division of Allergy and Clinical Immunology, Department of Internal Medicine,
University of South Florida College of Medicine, James A. Haley Veterans’ Medical Center, Tampa, Florida. “A New Symptom Based
Questionnaire for predicting the presence of Asthma” 2006;73:296-305. doi: 10.1159/000090141
[2] Tinkelman D, G, Price D, B, Nordyke R, J, Halbert R, J, Isonaka S, Nonikov D, Juniper E, F, Freeman D, Hausen T, Levy M, L, Østrem
A, van der Molen T, van Schayck C 2006;73:285-“Symptom-Based Questionnaire for Differentiating COPD and Asthma”, 295. doi:
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[3] Tinkelman D, G, Halbert R, J, Nordyke R, J, Isonaka S, Nonikov D, Juniper E, F, Freeman D, Hausen T, Levy M, L, Østrem A, van
der Molen T, van Schayck “Symptom-Based Questionnaire for Identifying COPD in Smokers, Respiration”
[4] S. Fathima and N. Hundewale, “Comparison of Classification Techniques- Support Vector Machines and Naive Bayes to predict
the Arboviral Disease-Dengue,” IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2011.
[5] S. Xu, Z. Zhang, D. Wang, J. Hu, X. Duan and T. Zhu,Cardiovascular “Risk Prediction Method Based on CFS Subset Evaluation
and Random Forest Classification Framework,” International Conference on Big Data Analysis, 2017
[6] S. Pouriyeh, S. Vahid, G. Sannino, G. D. Pietro and H. Arabnia, J. Gutierrez, “A Comprehensive Investigation and Comparison of
Machine Learning Techniques in the Domain of Heart Disease,” IEEE Symposium on Computers and Communication, 2017.
[7] Ken Farion Departments of Pediatrics and Emergency Medicine, University of Ottawa Ottawa, Canada Wojtek Michalowski, Szymon
Wilk1 , Dympna O’Sullivan Telfer School of Management, University of Ottawa Ottawa, Canada Stan Matwin School of Information
Technology and Engineering, University of Ottawa Ottawa, Canada Institute of Computer Science, Polish Academy of Sciences Warsaw,
Poland) “A Tree-based Decision Model to Support Prediction of the Severity of Asthma Exacerbations in Children”
[8] Vinayak Singh, Anant Gaikwad, Surendra Waso, Eknath Sawale, IJIRCCE Vol 4, Issue 3, March 2016, PP3253-3258 “Web Based e-
Health Systems and Services,”
[9] Statlog database: http://archive.ics.uci.edu/ml/machine-learningdatabases/statlog/heart/
[10] Cleveland database: http://archive.ics.uci.edu/ml/datasets/Heart+Disease