This document summarizes research on using knowledge-based systems and soft computing techniques in neuroscience. It provides an abstract for an article on this topic and then summarizes several other research papers that have applied expert systems, fuzzy logic, neural networks, and other computational approaches to problems in neurology, including diagnosing stroke type, modeling neuromuscular disorders, analyzing EEG data, and developing diagnostic systems for epilepsy. The document surveys this area and provides high-level summaries of several studies that have developed computational models and expert systems to assist with neurological diagnosis and analysis.
ARTIFICIAL NEURAL NETWORKS FOR MEDICAL DIAGNOSIS: A REVIEW OF RECENT TRENDSIJCSES Journal
Artificial Intelligence systems (especially computer-aided diagnosis and artificial neural networks) are increasingly finding many uses in medical diagnosis application in recent times. These methods are adaptive learning algorithms that are capable of handling multiple and heterogeneous types of clinical
data with a view of integrating them into categorized outputs. In this study, we briefly review and discuss the concept, capabilities, and applicability of artificial neural network techniques to medical diagnosis, through consideration of some selected physical and mental diseases. The study focuses on scholarly researches within the years, 2010 to 2019. Findings show that no electronic online clinical database exists in Nigeria and the Sub-Saharan countries, most review researches in this area focused mainly on physical diseases without considering mental illnesses, the application of ANN in mental and comorbid disorders have not been thoroughly studied, ANN models and algorithms consider mainly homogeneous input data sources and not heterogeneous input data sources, and ANN models on multi-objective output systems are few as compared to single output ANN models.
Current issues - International Journal of Computer Science and Engineering Su...IJCSES Journal
International Journal of Computer Science and Engineering Survey (IJCSES) is devoted to fields of Computer Science and Engineering surveys, tutorials and overviews. The IJCSES is a peer-reviewed, open access scientific journal published in electronic form as well as print form. The journal will publish research surveys, tutorials and expository overviews in computer science and engineering. Articles from supplementary fields are welcome, as long as they are relevant to computer science and engineering.
The evolving discipline of computational pain investigation provides modern gears to recognize the pain. This discipline uses Computational processing of difficult pain associated records and relies on “intelligent†Machine learning algorithms. By mining information from difficult pain associated records and generating awareness from this, facts will be simplified. Therefore, machine learning has the capability to encouragement the training and dealing of pain greatly. Indeed, the application of machine learning for pain investigation –associated non imaging problems has been mentioned in publications in scientific journals since 1940 2018. Among machine learning methods, a subset has so far been applied to pain research–related problems, SVMs, regression models, deep learning and several kinds of neural networks so far most often revealed in the pain literature. Machine learning receives increasing general interest and appears to penetrate many parts of everyday life and natural sciences. This affinity is likely to spread to pain investigation. The current review objectives to familiarize pain area professionals with the methods and current applications of machine learning in pain investigation, possibly simplifying the awareness of the methods in current and future assignments. Tarun Jaiswal | Sushma Jaiswal ""Deep Learning Based Pain Treatment"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23639.pdf
Paper URL: https://www.ijtsrd.com/computer-science/cognitive-science/23639/deep-learning-based-pain-treatment/tarun-jaiswal
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.
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.
Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light. This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.
Medical System and Artificial Intelligence: How AI assists hospital-dependent...AI Publications
The main objective of this study is to examine how artificial intelligence assists hospital-dependent patients and explore the role of artificial intelligence in the medical system. Hospital-dependent patients have become common in current society due to the elderly with multiple chronic conditions and the COVID-19 infection patient. Thus, it is undeniable that the medical field is lacking healthcare workers. However, in a globalized world, artificial intelligence, the field of science and engineering technology that makes intelligent machines perform given tasks, is chosen to be used as a tool for assisting hospital-dependent patients and collecting databases from the patients. Nevertheless, the paper will cover the use of artificial intelligence in the medical system, hospital-dependent patients as well as provide both positive and negative aspects and the comparison of using artificial intelligence instead of human intelligence. To conclude, we detail how artificial intelligence can take part in the medical system, assist hospital-dependent patients and study further the future of artificial intelligence in the medical system.
Neural Network Based Classification and Diagnosis of Brain HemorrhagesWaqas Tariq
The classification and diagnosis of brain hemorrhages has work out into a great importance diligence in early detection of hemorrhages which reduce the death rates. The purpose of this research was to detect brain hemorrhages and classify them and provide the patient with correct diagnosis. A possible solution to this social problem is to utilize predictive techniques such as sparse component analysis, artificial neural networks to develop a method for detection and classification. In this study we considered a perceptron based feed forward neural network for early detection of hemorrhages. This paper attempts to spot on consider and talk about Computer Aided Diagnosis (CAD) that chiefly necessitated in clinical diagnosis without human act. This paper introduces a Region Severance Algorithm (RSA) for detection and location of hemorrhages and an algorithm for finding threshold band. In this paper different data sets (CT images) are taken from various machines and the results obtained by applying our algorithm and those results were compared with domain expert. Further researches were challenged to originate different models in study of hemorrhages caused by hyper tension or by existing tumor in the brain.
ARTIFICIAL NEURAL NETWORKS FOR MEDICAL DIAGNOSIS: A REVIEW OF RECENT TRENDSIJCSES Journal
Artificial Intelligence systems (especially computer-aided diagnosis and artificial neural networks) are increasingly finding many uses in medical diagnosis application in recent times. These methods are adaptive learning algorithms that are capable of handling multiple and heterogeneous types of clinical
data with a view of integrating them into categorized outputs. In this study, we briefly review and discuss the concept, capabilities, and applicability of artificial neural network techniques to medical diagnosis, through consideration of some selected physical and mental diseases. The study focuses on scholarly researches within the years, 2010 to 2019. Findings show that no electronic online clinical database exists in Nigeria and the Sub-Saharan countries, most review researches in this area focused mainly on physical diseases without considering mental illnesses, the application of ANN in mental and comorbid disorders have not been thoroughly studied, ANN models and algorithms consider mainly homogeneous input data sources and not heterogeneous input data sources, and ANN models on multi-objective output systems are few as compared to single output ANN models.
Current issues - International Journal of Computer Science and Engineering Su...IJCSES Journal
International Journal of Computer Science and Engineering Survey (IJCSES) is devoted to fields of Computer Science and Engineering surveys, tutorials and overviews. The IJCSES is a peer-reviewed, open access scientific journal published in electronic form as well as print form. The journal will publish research surveys, tutorials and expository overviews in computer science and engineering. Articles from supplementary fields are welcome, as long as they are relevant to computer science and engineering.
The evolving discipline of computational pain investigation provides modern gears to recognize the pain. This discipline uses Computational processing of difficult pain associated records and relies on “intelligent†Machine learning algorithms. By mining information from difficult pain associated records and generating awareness from this, facts will be simplified. Therefore, machine learning has the capability to encouragement the training and dealing of pain greatly. Indeed, the application of machine learning for pain investigation –associated non imaging problems has been mentioned in publications in scientific journals since 1940 2018. Among machine learning methods, a subset has so far been applied to pain research–related problems, SVMs, regression models, deep learning and several kinds of neural networks so far most often revealed in the pain literature. Machine learning receives increasing general interest and appears to penetrate many parts of everyday life and natural sciences. This affinity is likely to spread to pain investigation. The current review objectives to familiarize pain area professionals with the methods and current applications of machine learning in pain investigation, possibly simplifying the awareness of the methods in current and future assignments. Tarun Jaiswal | Sushma Jaiswal ""Deep Learning Based Pain Treatment"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23639.pdf
Paper URL: https://www.ijtsrd.com/computer-science/cognitive-science/23639/deep-learning-based-pain-treatment/tarun-jaiswal
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.
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.
Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light. This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.
Medical System and Artificial Intelligence: How AI assists hospital-dependent...AI Publications
The main objective of this study is to examine how artificial intelligence assists hospital-dependent patients and explore the role of artificial intelligence in the medical system. Hospital-dependent patients have become common in current society due to the elderly with multiple chronic conditions and the COVID-19 infection patient. Thus, it is undeniable that the medical field is lacking healthcare workers. However, in a globalized world, artificial intelligence, the field of science and engineering technology that makes intelligent machines perform given tasks, is chosen to be used as a tool for assisting hospital-dependent patients and collecting databases from the patients. Nevertheless, the paper will cover the use of artificial intelligence in the medical system, hospital-dependent patients as well as provide both positive and negative aspects and the comparison of using artificial intelligence instead of human intelligence. To conclude, we detail how artificial intelligence can take part in the medical system, assist hospital-dependent patients and study further the future of artificial intelligence in the medical system.
Neural Network Based Classification and Diagnosis of Brain HemorrhagesWaqas Tariq
The classification and diagnosis of brain hemorrhages has work out into a great importance diligence in early detection of hemorrhages which reduce the death rates. The purpose of this research was to detect brain hemorrhages and classify them and provide the patient with correct diagnosis. A possible solution to this social problem is to utilize predictive techniques such as sparse component analysis, artificial neural networks to develop a method for detection and classification. In this study we considered a perceptron based feed forward neural network for early detection of hemorrhages. This paper attempts to spot on consider and talk about Computer Aided Diagnosis (CAD) that chiefly necessitated in clinical diagnosis without human act. This paper introduces a Region Severance Algorithm (RSA) for detection and location of hemorrhages and an algorithm for finding threshold band. In this paper different data sets (CT images) are taken from various machines and the results obtained by applying our algorithm and those results were compared with domain expert. Further researches were challenged to originate different models in study of hemorrhages caused by hyper tension or by existing tumor in the brain.
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.
ONTOLOGY BASED TELE-HEALTH SMART HOME CARE SYSTEM: ONTOSMART TO MONITOR ELDERLY csandit
The population ageing is a demographical phenomenon that will intensify in the upcoming
decades, leading to an increased number of older persons that live independently. These elderly
prefer to stay at home rather than going to special health care association. Thus, new telehealth
smart home care systems (TSHCS) are needed in order to provide health services for
older persons and to remotely monitor them. These systems help to keep patients safe and to
inform their relatives and the medical staff about their status. Although various types of TSHCS
already exist, they are environment dependent and scenario specific. Therefore, the aim of this
paper is to propose sensors and scenarios independent flexible context aware and distributed
TSHCS based on standardized e-Health ontologies and multi-agent architecture.
A neural is a system hardware or software that is patterned to function and was named after the neurons in the brains of humans. A neural network is known to involve several huge processors that are arranged and work in the parallel format for effectiveness. Applications include: handwriting recognition, oil data analysis, weather forecast prediction and face recognition.
Cervical Smear Analyzer (CSA) Expert System for identification of cervical ce...ijtsrd
The primary objective of this research work is to develop an expert system for identification & classification of the cervical cells in the images of the slides of Papanicolaou smear test, which is done for screening of cervical cancer. The expert system can serve as a potential tool for mass level screening of cervical cancer by characterization and classification of Papanicolaou smear images. The Expert system presented in this work is powered by a novel hierarchical probabilistic artificial neural network that works along with the knowledgebase of novel benchmark database of digitized cervical cells. The primary purpose of employing expert systems in medicine is creation of such artificially intelligent systems which can provide assistance to a medical doctor in delivering expert diagnosis. These artificial intelligent systems support the clinical decision making by anticipating the diagnostic results once they are trained using previously acquired training data. The use of Artificial intelligence in medicine has shown substantial progress in achieving timely, reliable diagnosis and more precise treatment of many diseases.The expert system developed in this work exhibited a competence of about 92% which has been evaluated by comparing its results with the identification & classification of cervical cells by human experts. Abid Sarwar"Cervical Smear Analyzer (CSA) Expert System for identification of cervical cells in Papanicolaou smear test" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7047.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/7047/cervical-smear-analyzer-csa-expert-system-for-identification-of-cervical-cells-in-papanicolaou-smear-test/abid-sarwar
A comparative study on remote tracking of parkinson’s disease progression usi...ijfcstjournal
In recent years, applications of data mining method
s are become more popular in many fields of medical
diagnosis and evaluations. The data mining methods
are appropriate tools for discovering and extractin
g
of available knowledge in medical databases. In thi
s study, we divided 11 data mining algorithms into
five
groups which are applied to a dataset of patient’s
clinical variables data with Parkinson’s Disease (P
D) to
study the disease progression. The dataset includes
22 properties of 42 people that all of our algorit
hms
are applied to this dataset. The Decision Table wit
h 0.9985 correlation coefficients has the best accu
racy
and Decision Stump with 0.7919 correlation coeffici
ents has the lowest accuracy.
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
DE-IDENTIFICATION OF PROTECTED HEALTH INFORMATION PHI FROM FREE TEXT IN MEDIC...ijsptm
Medical health records often contain clinical investigations results and critical information regarding patient health conditions. In these medical records, along with patient health information, patient Protected Health Information (PHI) such as names, locations and date information can co-exist. As per Health Insurance Portability and Accountability Act (HIPAA), before sharing the medical records with researchers and others, all types of PHI information needs to be de-identified. Manual de-identification through human annotators is laborious and error prone, hence, a reliable automated de-identification system is need of the hour.
In this work, various state of the art techniques for de-identification of patient notes in electronic health records were analyzed for their performance, based on the performance quoted in the literature, NeuroNER was selected to de-identify Indian Radiology reports. NeuroNER is a named-entity recognition text de-identification tool developed by Massachusetts Institute of Technology (MIT). This tool is based on the Artificial Neural Networks written in Python and uses Tensorflow machine-learning framework and it comes with five pre-trained models.
To test the NeuroNER models on Indian context data such as name of the person and place, 3300 medical records were simulated. Medical records were simulated by extracting clinical findings, remarks from MIMIC-III data set. For collection of all the relevant Indian data, various websites were scraped to include Indian names, Indian locations (all towns and cities), and Indian Hospital and unit names. During the testing of NeuroNER system, we observed that some of the Indian data such as name, location, etc. were not de-identified satisfactorily. To improve the performance of NeuroNER on Indian context data, along with the existing NeuroNER pre-trained model, a new pre-trained model was added to handle Indian medical reports. Medical dictionary lookup was used to reduce number of misclassifications. Results from all four pre-trained models and the model trained on Indian simulated data were concatenated and final PHI token list was generated to anonymize the medical records to obtain de-identified records. Using this approach, we improved the applicability of the NeuroNER system to Indian data and improved its efficiency and reliability. 2000 simulated reports were used for transfer learning as training set, 1000 reports were used for test set and 300 reports were used for validation (unseen) set.
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
Supervised Multi Attribute Gene Manipulation For Cancerpaperpublications3
Abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems.
They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery.
International Journal of Data Mining & Knowledge Management Process - novemb...IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
The purpose of this book is to provide an overview of medical image processing and related health care services in variant diseases like Alzheimer’s disease, glaucoma, mild cognitive impairment, dementia, and other neurodegenerative syndromes. It is anticipated that it will be useful for research scientists to capture recent developments and to spark innovative ideas within the medical image processing domain. With an emphasis on both the basic and advanced applications of medical imaging, this book covers several unique concepts like optimized image fusion, ophthalmic hashing, and linear bootstrap aggregating that have been graphically represented to improve readability, such as the optimized image fusion introduced in chapter 1, linear bootstrap aggregating discussed in Chapter 2 and ophthalmic hashing that is proposed in Chapter 4. The remainder of the book emphases on the area application-orientated image fusions, which cover the numerous expanses of medical image processing and its applications.
eHealth Governance in a Local Organisation. The Experience from Pompidou Hospital. Degoulet P. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)
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.
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 ONLINE EXPERT SYSTEM FOR PSYCHIATRIC DIAGNOSISijaia
Expert systems are programs that use artificial intelligence techniques, and simulate the performance of the
human expert in a given area of expertise by collecting and using one or more expert information and
expertise in a particular field. We developed declarative, online procedural rule-based expert system
models for psychological diseases diagnosis and classification. The constructed system exploited computer
as an intelligent and deductive tool. This system diagnoses and treats more than four types of psychiatric
diseases, i.e., depression, anxiety disorder, obsessive-compulsive disorder, and hysteria. The system helps
psychology practitioner and doctors to diagnose the condition of a patient efficiently and in short time. It is
also very useful for the patients who cannot go to a doctor because they cannot afford the cast, or they do
not have a psychological clinic in their area, or they are ashamed of discussing their situation with a
doctor. The system consists of program codes that make a logic decision to classify the problem of the
patient. The methodology for developing the declarative model was based on the backward chaining, also
called goal-driven reasoning, where knowledge is represented by a set of IF-THEN production rules. The
declarative programs were written in the PROLOG. While the design of the procedural model was based
on using common languages like PHP, JavaScript, CSS, and HTML. The user of the system will enter the
symptoms of the patients through the user interface and the program executes. Then the program links the
symptoms to the pre-programmed psychological diseases, and will classify the disease and recommend
treatment.
The proposed online system link: http://esp-online.site/
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.
ONTOLOGY BASED TELE-HEALTH SMART HOME CARE SYSTEM: ONTOSMART TO MONITOR ELDERLY csandit
The population ageing is a demographical phenomenon that will intensify in the upcoming
decades, leading to an increased number of older persons that live independently. These elderly
prefer to stay at home rather than going to special health care association. Thus, new telehealth
smart home care systems (TSHCS) are needed in order to provide health services for
older persons and to remotely monitor them. These systems help to keep patients safe and to
inform their relatives and the medical staff about their status. Although various types of TSHCS
already exist, they are environment dependent and scenario specific. Therefore, the aim of this
paper is to propose sensors and scenarios independent flexible context aware and distributed
TSHCS based on standardized e-Health ontologies and multi-agent architecture.
A neural is a system hardware or software that is patterned to function and was named after the neurons in the brains of humans. A neural network is known to involve several huge processors that are arranged and work in the parallel format for effectiveness. Applications include: handwriting recognition, oil data analysis, weather forecast prediction and face recognition.
Cervical Smear Analyzer (CSA) Expert System for identification of cervical ce...ijtsrd
The primary objective of this research work is to develop an expert system for identification & classification of the cervical cells in the images of the slides of Papanicolaou smear test, which is done for screening of cervical cancer. The expert system can serve as a potential tool for mass level screening of cervical cancer by characterization and classification of Papanicolaou smear images. The Expert system presented in this work is powered by a novel hierarchical probabilistic artificial neural network that works along with the knowledgebase of novel benchmark database of digitized cervical cells. The primary purpose of employing expert systems in medicine is creation of such artificially intelligent systems which can provide assistance to a medical doctor in delivering expert diagnosis. These artificial intelligent systems support the clinical decision making by anticipating the diagnostic results once they are trained using previously acquired training data. The use of Artificial intelligence in medicine has shown substantial progress in achieving timely, reliable diagnosis and more precise treatment of many diseases.The expert system developed in this work exhibited a competence of about 92% which has been evaluated by comparing its results with the identification & classification of cervical cells by human experts. Abid Sarwar"Cervical Smear Analyzer (CSA) Expert System for identification of cervical cells in Papanicolaou smear test" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7047.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/7047/cervical-smear-analyzer-csa-expert-system-for-identification-of-cervical-cells-in-papanicolaou-smear-test/abid-sarwar
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In recent years, applications of data mining method
s are become more popular in many fields of medical
diagnosis and evaluations. The data mining methods
are appropriate tools for discovering and extractin
g
of available knowledge in medical databases. In thi
s study, we divided 11 data mining algorithms into
five
groups which are applied to a dataset of patient’s
clinical variables data with Parkinson’s Disease (P
D) to
study the disease progression. The dataset includes
22 properties of 42 people that all of our algorit
hms
are applied to this dataset. The Decision Table wit
h 0.9985 correlation coefficients has the best accu
racy
and Decision Stump with 0.7919 correlation coeffici
ents has the lowest accuracy.
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neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
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classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
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Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
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They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery.
International Journal of Data Mining & Knowledge Management Process - novemb...IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
The purpose of this book is to provide an overview of medical image processing and related health care services in variant diseases like Alzheimer’s disease, glaucoma, mild cognitive impairment, dementia, and other neurodegenerative syndromes. It is anticipated that it will be useful for research scientists to capture recent developments and to spark innovative ideas within the medical image processing domain. With an emphasis on both the basic and advanced applications of medical imaging, this book covers several unique concepts like optimized image fusion, ophthalmic hashing, and linear bootstrap aggregating that have been graphically represented to improve readability, such as the optimized image fusion introduced in chapter 1, linear bootstrap aggregating discussed in Chapter 2 and ophthalmic hashing that is proposed in Chapter 4. The remainder of the book emphases on the area application-orientated image fusions, which cover the numerous expanses of medical image processing and its applications.
eHealth Governance in a Local Organisation. The Experience from Pompidou Hospital. Degoulet P. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)
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.
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 ONLINE EXPERT SYSTEM FOR PSYCHIATRIC DIAGNOSISijaia
Expert systems are programs that use artificial intelligence techniques, and simulate the performance of the
human expert in a given area of expertise by collecting and using one or more expert information and
expertise in a particular field. We developed declarative, online procedural rule-based expert system
models for psychological diseases diagnosis and classification. The constructed system exploited computer
as an intelligent and deductive tool. This system diagnoses and treats more than four types of psychiatric
diseases, i.e., depression, anxiety disorder, obsessive-compulsive disorder, and hysteria. The system helps
psychology practitioner and doctors to diagnose the condition of a patient efficiently and in short time. It is
also very useful for the patients who cannot go to a doctor because they cannot afford the cast, or they do
not have a psychological clinic in their area, or they are ashamed of discussing their situation with a
doctor. The system consists of program codes that make a logic decision to classify the problem of the
patient. The methodology for developing the declarative model was based on the backward chaining, also
called goal-driven reasoning, where knowledge is represented by a set of IF-THEN production rules. The
declarative programs were written in the PROLOG. While the design of the procedural model was based
on using common languages like PHP, JavaScript, CSS, and HTML. The user of the system will enter the
symptoms of the patients through the user interface and the program executes. Then the program links the
symptoms to the pre-programmed psychological diseases, and will classify the disease and recommend
treatment.
The proposed online system link: http://esp-online.site/
Nlp based retrieval of medical information for diagnosis of human diseaseseSAT Journals
Abstract NLP Based Retrieval of Medical Information is the extraction of medical data from narrative clinical documents. In this paper, we provide the way to diagnose diseases with the help of natural language interpretation and classification techniques. However extraction of medical information is difficult task due to complex symptom names and complex disease names. For diagnosis we will be using two approaches, one is getting disease names with the help of classifiers and another way is using the patterns with the help of NLP for getting the information related to diseases. These both approaches will be applied according to the question type. Keywords: NLP, narrative text, extraction, medical information, expert system
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games – tasks which would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in healthcare. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades – and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. Tarun Jaiswal | Sushma Jaiswal ""Deep Learning in Medicine"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23641.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23641/deep-learning-in-medicine/tarun-jaiswal
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.
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1. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.4, No.2, May 2015
DOI :10.5121/ijscai.2015.4201 1
KNOWLEDGEBASE SYSTEMS IN NEURO SCIENCE - A
STUDY
Dr. D.K. Sreekantha 1
, T.M. Girish 2
and Dr. R.V.Kulkarni3
1
Department of Computer Science & Engineering, NMAMIT, Nitte, Karnataka
2
Dept. of CS, Basaveshwar Science College, Bagalkot, Karnataka
3
Head, Dept. of Computer Studies, CSIBER, Kolhapur, Maharastra
ABSTRACT
The improvement of health and nutritional status of the society has been one of the thrust areas for social
developments programmes of the country. The present states of healthcare facilities in India are inadequate
when compared to international standards. The average Indian spending on healthcare is much below the
global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the
fastest in the world. The prospects for Indian healthcare are to the tune of USD 40 billion, while global
market is USD 1660 trillion. India has all the prospects to become medical tourism destination of the
world, because it has a large pool of low-cost scientifically trained technical personal and is one of the
favoured counties for cost effective healthcare. As per the reports of Global Burden of Neurological
Disorders Estimations and Projections survey there is big shortage of neurologist in India and around the
world. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to
gain expertise in Neuro Science and treat patients like expert neurologist. This paper aims to survey the
Soft Computing techniques in treating neural patient’s problems used throughout the world
KEYWORDS
Neurology, Expert System, Soft Computing
1. INTRODUCTION
India is one of the favoured countries for cost effective healthcare and has all the potential to
become medical tourism destination of the world. The hospital and nursing home industry is
growing at rate of 20% annually. India is one of the top 3 countries, where companies plan to
spend most research and development funds over the next 3 years. The Indian clinical community
is populated with English speaking, western-trained graduates. Two thirds of healthcare spending
to the tune of USD 20 billion is out of pocket in private spending. India has 280 million strong
middle and upper middle class population with 10–12 million high income groups, which could
afford the lifestyle of their western counterparts. (Source : Economist Intelligence Unit, World
Industry Outlook – Healthcare and Pharmacuticals, 2010 CAGR Compound annual growth rate).
According to AAN2010 Practice Profile Form neurologists have devoted on average 42.3 hours a
week in practice 2010. The number of visits to hospital has increased to 81.8 on average per week
in 2010. These facts will signify the need for neurologist services in developing country like India
around the world. Hence the researchers are focusing on building an innovative IT based solution
to help novice doctors to treat neural patients in rural areas. During the last decade much research
effort has been devoted to the development of expert systems to cope with complex medical
decision-making.
2. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.4, No.2, May 2015
2
An expert system is "an embodiment within a computer of a knowledge-based component, from
an expert skill, in such a form that the system can offer intelligent advice or make an intelligent
decision about a processing function." such a system uses expert knowledge to attain high levels
of performance in a narrow problem area. In the process of diagnosis, neurologists collect
historical data, neurologic signs, and symptoms to arrive at a "best guess" as to stroke type, which
then forms the basis for performing further diagnostic procedures such as computed tomography
(CT scan) or cardiologic or cerebrovascular tests.
2. SURVEY OF LITERATURE
The present work comprises an exhaustive survey of relevant literature of most relevant articles
on Soft Computing techniques applied in Neuro Science from reputed journals from IEEE
Transactions, Springer and Elsevier publishers and proceedings of international conferences.
Some selected articles from this survey are discussed by way of illustration.
Klaus Spitzer, Andreas Thie, Louis R. Caplan and Klaus Kunze [1] (1989) have designed a
prototype MICROSTROKE expert system to categorize and diagnose stroke type based on
clinical information. The knowledge base of MICROSTROKE includes information from large
stroke registries. The system first queries the physician-user for details of the patient's history,
information about the onset of stroke, accompanying symptoms and pertinent neurologic findings
and then sums the individual data items, factors in the a priori odds, and arrives at the
probabilities of different stroke types for a given patient. Stroke type diagnoses by
MICROSTROKE were correct in 72.8% of 250 cases in the Hamburg Stroke Data Bank, stroke
types can be of prime importance. Authors presented MICROSTROKE, the prototype of an
expert system for computer-supported stroke type diagnosis based only on clinical and historical
patient data available at the bedside; MICROSTROKE serves as an aid in the diagnostic work-up
of stroke as both a stroke patient data bank and as an educational tool in clinical teaching.
Cruz J, Barahona P, Figueiredo A.P, Veloso M and Carvalho M [2] (1994) have discussed a new
knowledge-based system called DARE for the diagnosis of neuromuscular disorders that
performs anatomic-physiological reasoning on a deep causal-functional model of the domain
knowledge. These characteristics make the system more flexible and general than similar systems
in this domain and favour its potential use in different local environments. This paper also
discusses the preliminary evaluation of the system performed in the European project ESTEEM,
as well as the work still required to make it a real clinical application. The current version of
DARE already achieved a quite acceptable diagnostic performance and many improvements can
done in the near future such as a need to extend the knowledge model either quantitatively
(representing more anatomical structures and functionalities) or qualitatively (including explicitly
the etiologic and temporal knowledge) and to support the adopted reasoning methods with more
formalized models (allowing the formal definition of reasoning tasks for diagnosis, prognosis and
patient treatment).
Christophe S. Herrmann [3] (1995) combined Fuzzy Logic, neural network and an expert system
to build a hybrid diagnosis system, a new approach to the acquisition of knowledge bases. This
system consists of a fuzzy expert system with a dual source knowledge base. Two sets of rules are
acquired, inductively from given examples and deductively formulated by a physician. A fuzzy
neural network serves to learn from sample data and allows extracting fuzzy rules for the
knowledge base. The diagnosis of electroencephalograms by interpretation of graph elements
serves as visualization for author’s approach. Preliminary results demonstrate the promising
possibilities offered.
3. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.4, No.2, May 2015
3
The hybrid system, described in this article, introduces the following new paradigms of modelling
the cognitive task of diagnosis, Instead of either acquiring the whole knowledge base
automatically from examples, being an inductive learning method.
Leda Cosmides and John Tooby [4] (1997) have authored an article The Cognitive Neuroscience
of Social Reasoning. Cognitive scientists needed theoretical guidance that is grounded in
something beyond intuition. Authors need evolutionary biology's "adaptationist program", a
research strategy in which theories of adaptive function are key inferential tools, used to identify
and investigate the design of evolved systems. Using research on how human’s reason about
social exchange. The authors illustrated how theories of adaptive function can generate detailed
and highly testable hypotheses about the design of computational machines in the human mind
and reviewed research that tests for the presence of these machines. This research suggests that
the human computational architecture contains an expert system designed for reasoning about
cooperation for mutual benefit, with a subroutine specialized for cheaper detection. By using a
computational theory specifying the adaptive problems entailed by social exchange authors team
were able to predict, in advance, that certain very subtle changes in the content and context would
produce dramatic differences in how people reason. Authors concluded that the adaptationist
program is cognitive neuroscience's best hope for achieving this goal.
Jean-Marc Fellous, Jorge L. Armony and Joseph E. LeDoux [5] (2000) have published the article
Emotional Circuits and Computational Neuroscience. Emotion is clearly an important aspect of
the mind, yet it has been largely ignored by the "brain and mind (cognitive) sciences" in modern
times. However, there are signs that this is beginning to change. Authors discussed some issues
about the nature of emotion, describe what is known about the neural basis of emotion and
consider some efforts that have been made to develop computer-based models of different aspects
of emotion. It is important to distinguish between emotional experiences and the underlying
processes that lead to emotional experiences. One of the stumbling blocks to an adequate
scientific approach to emotion has been the focus of the field on constructing theories of the
subjective aspects of emotion. Studies of the neural basis of emotion and emotional learning have
instead focused on how the brain detects and evaluates emotional stimuli and how, on the basis of
such evaluations, emotional responses are produced. Computational approaches to emotional
processing are both possible and practical. Although relatively few models currently exist, this
situation is likely to change as researchers begin to realize the opportunities that are present in
this too-long neglected area.
Emmanuil Marakakis, Kostas Vassilakis, Emmanuil Kalivianakis, Sifis Micheloyiannis [6]
(2005) have designed Expert System for Epilepsy with Uncertainty, that derives differential
diagnosis about epilepsy cases in childhood, according to international classifications. This expert
system is intended to be used as a consultation system by neurologists. The physician specialist
can update the knowledge base of the system through a user friendly interface. Uncertainty is
handled by using weights and certainty factors. Meta-rules drive the reasoning process of the
system. The initial evaluation results of the system are very promising, i.e. 83.3% successful
diagnosis. HIPPOCRAT-EES preliminary results are satisfactory compared with the results of the
systems. The results from the evaluation of the decision support system in are as follows. The
system has derived correct diagnosis in 85.2% of patient cases, partial successful diagnosis in
8.2% of patient cases and absolute incorrect diagnosis in 6.6% of patient cases. The diagnosis of
the DSS is more accurate than the one of HIPPOCRAT-EES. On the other hand the DSS does not
derive alternative diagnoses as HIPPOCRAT-EES does. In addition, the KB of the DSS cannot be
updated by physicians like HIPPOCRAT-EES. The diagnosis of the system has been compared
with the diagnosis of three experts. The evaluation results are 72% correct diagnoses, 8% partially
correct diagnoses and 20% incorrect diagnoses.
4. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.4, No.2, May 2015
4
Blacksmiths, Neurons Mauro Adenzato and Francesca Garbarini [7] (2006) authored the paper
Cognitive Science, Neuroscience and Anthropology - A Journey among Robots. In recent years,
neurophysiological and psychological research has highlighted a pragmatic version of the theory
of knowledge, a version in which the concept of simulation has been found to play a crucial role.
In fact, research on canonical and mirror neurons has shown that as if simulative schema is
required to perceive, categorize and understand the meaning of the external world. The present
study compares the cognitive paradigm of embodied cognition with Pierre Bourdieu’s practice
theory. Specifically, cognitive processes and cultural mechanisms are described as phenomena
that emerge from the dynamic interaction that exists between people’s practical abilities and the
structure of the local environment in which they act and live. A pragmatic conception of
knowledge has also emerged in the field of ethnological investigation. Indeed, the concepts of
resonance and empathy have proven to be essential instruments for ethnological knowledge. With
this new view of the relationship between mind and body and between culture and nature, there
are now greater opportunities for conducting interdisciplinary research in the natural sciences and
social sciences, research aimed at reconstituting the fracture that has existed for too long now
between humans as biological and cultural beings. As Jacob and Jeannerod (2005) observed, the
mirror neuron system is well designed for representing an agent’s motor intention, but not the
agent’s prior intention to execute an action. A discussion on the theoretical distinction between
motor intention and prior intention goes beyond the aims of the present work. Authors referred to
Becchio, Adenzato and Bara (2006) for a more exhaustive analysis of the issue.
Badri Adhikari, Md. Hasan Ansari, Priti Shrestha and Susma Pant [8] (2008) have developed a
Neurology Diagnosis System, which is concerned about the construction of a web-based expert
system. The objective of the system is to help the diagnosis process of neurology doctors.
Neurology is a medical specialty that deals with disorders of the nervous system. Doctors will use
the website as a helpful tool to diagnose their patients. The web application will collect rules of
the neurology domain and cases of the patients. Integrating the techniques of rule-based reasoning
and case-based reasoning a hybrid system can be constructed. The system will use the rules and
cases to achieve the objective of assisting the decision making process of the domain experts. The
proposed system will prove effective, efficient and will establish itself as a valuable asset of the
department and the hospital. Precise analysis of return on investment (ROI) and breakeven
analysis is difficult at this proposing stage of an academic project. It can be assured that the
project will prove economically feasible.
Murad Alaqtash, Huiying Yu, Richard Brower, Amr Abdelgawad, Eric Spier, and Thompson
Sarkodie-Gyan [9] (2010) have authored Application of Wearable Miniature Non-invasive
Sensory System in Human Locomotion using Soft Computing Algorithm. The authors have
designed and tested a wearable miniature non-invasive sensory system for the acquisition of gait
features. The sensors are placed on anatomical segments of the lower limb, and motion data was
then acquired in conjunction with electromyography (EMG) for muscle activities, and
instrumented treadmill for ground reaction forces (GRF). A relational matrix was established
between the limb-segment accelerations and the gait phases. This algorithm offers the possibility
to perform functional comparisons using different sources of information. It can provide a
quantitative assessment of gait function. This algorithm has clearly illustrated the possibility to
perform functional comparisons by using different sources of information.
The fuzzy similarity methodology depicts distinctions between the reference able-bodied and the
randomized test subjects within with a membership grade of belonging. This novel algorithm may
offer very reliable and efficient tools for the evaluation and assessment of gait function in several
ways: By building a rule-based system depicts the strength of relation between muscle activities,
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limb-segment accelerations, and gait phases. By comparing the reference muscle activities within
gait phases with a randomized input-matrix through a fuzzy similarity algorithm.
Youssouf EL ALLIOUI and Omar EL BEQQALI [10] (2010) have authored the article
O’Neurolog – Building, an Ontology for Neurology in Mobile Environment, The (context aware
services). A crucial requirement for the context-aware service provisioning is the dynamic
retrieval and interaction with local resources, i.e., resource discovery. The high degree of
dynamicity and heterogeneity of mobile environments requires rethinking and/or extending
traditional discovery solutions to support more intelligent service search and retrieval,
personalized to user context conditions. Authors research work aims at providing suitable
answering mechanisms of mobile requests by taking into account user contexts (preferences,
profiles, physical location, temporal information…). This paper proposes ontology, called
O’Neurolog, to capture semantic knowledge a valuable in Neurology domain in order to assist
users (doctor, patient, administration …) when querying Neurology knowledge bases in mobile
environment. First, authors designed a domain specific ontology, called O’Neurolog that
incorporates concepts drawn from raw data and expert knowledge. In fact, data and knowledge
discovery is a crucial activity in pervasive environments where mobile users need to be provided
with personalized results due to limited physical characteristics of portable devices. Another
interesting future issue authors envision dealing which is the resolution of conflicts that may arise
between value or priority preferences. Authors believe that a possible approach may be the
definition of meta-preferences, as authors have began the formalization in section.
Faran Baig, M. Saleem Khan, Yasir Noor and M. Imran [11] (2011) have designed model of
Fuzzy Logic Medical Diagnosis Control System, This research work addresses the medical
diagnosis regarding the normality of a human function in human brain and the diagnosis of
haemorrhage and brain tumour. It enhances the control strategies in the medical field to diagnose
a disease. The simulation results are found in agreement with the design based calculated results.
This research work proposes to develop a control system to enhance the efficiency to diagnose a
disease related to human brain. Both the design model and simulation result are same. The
designed system can be extended for any number of inputs. Normal, haemorrhage and the brain
tumour all depend on the inputs protein, red blood cell, lymphocytes, neutrophils and eosinophils.
Authors defined this system for any number of inputs. As the inputs are the blood cells and the
designed system use five blood cells as inputs, similarly authors defined this system more than
five Inputs to get more efficient human diagnose results. The design work was being carried out
to design state of the art fuzzy logic medical diagnosis control system in future using FPGAs.
Dragan Simić, Svetlana Simić, and Ilija Tanackov [12] (2011) authored the article An Approach
of Soft Computing Applications in Clinical Neurology, this paper briefly introduces various soft
computing techniques and presents miscellaneous applications in clinical neurology domain.
Authors presented applications of soft computing models of the cutting edge researches in
neurology domain, specifically for EMG and EEG signals. This paper only indicates some
researches based on hybrid soft computing and expert and decision support systems. Also,
researches on implementation of different artificial intelligence techniques – hybrid soft
computing methods can be applied to almost all medical domains, neurology included.
Rajdeep Borgohain and Sugata Sanyal [13] (2011) designed Rule Based Expert System for
Cerebral Palsy Diagnosis. The use of artificial intelligence is finding prominence not only in core
computer areas, but also in cross disciplinary areas including medical diagnosis. The expert
system takes user input and depending on the symptoms of the patient, diagnoses and if the
patient is suffering from Cerebral Palsy. The expert system also classifies the Cerebral Palsy as
mild, moderate or severe based on the presented symptoms. Authors have discussed the design
and implementation of a rule based Expert System for Cerebral Palsy Diagnosis. This expert
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system helps to diagnose Cerebral Palsy and classify it as mild, moderate or severe. In the
implementation, authors have taken the most classical symptoms of Cerebral Palsy and given a
weight age to each of the symptom and according to the feedback given by the user.
The expert system can go a great deal in supporting the decision making process of medical
professionals and also help parents having children with Cerebral Palsy to assess their children
and to take appropriate measures to manage the disease.
Imianvan Anthony Agboizebeta and Obi Jonathan Chukwuyeni [14] (2012) have authored
Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means Multiple sclerosis, often
called MS, is a disease that affects the central nervous system (the brain and spinal cord). Myelin
provides insulation for nerve cells improves the conduction of impulses along the nerves and is
important for maintaining the health of the nerves. In multiple sclerosis, inflammation causes the
myelin to disappear. Genetic factors, environmental issues and viral infection may also play a role
in developing the disease. MS is characterized by life threatening symptoms such as; loss of
balance, hearing problem and depression. This paper presents a diagnostic fuzzy cluster means
system to help in diagnosis of Multiple sclerosis using a set of symptoms. This advanced system
which uses clustered data set is more precise than the traditional system. The classification,
verification and matching of symptoms to the two groups of clusters (Relapsing/remitting
multiple sclerosis and Primary Progressive Multiple Sclerosis) was necessary especially in some
complex scenarios. This paper demonstrates the practical application of ICT (Information and
communication technology) in the domain of diagnostic pattern appraisal in medicine by
determining the extent of membership of individual symptoms. The model proposed allows for
the classification of matching of cluster groups to multiple sclerosis symptoms. The fuzzy-cluster
means model proposed in this paper appears to be a more useful.
Sujit Das, Pijush Kanti Ghosh and Samarjit Kar [15] (2012) have authored Hypertension
Diagnosis: A Comparative Study using Fuzzy Expert System and Neuro Fuzzy System.
Hypertension is called the silent killer because it has no symptoms and can cause serious trouble
if left untreated for a long time. It has a major role for stroke, heart attacks, heart failure,
aneurysms of the arteries, peripheral arterial diseases, chronic kidney disease etc. Then this paper
presents a comparative study between fuzzy expert system (FES) and feed forward back
propagation based neuro fuzzy system (NFS) for hypertension diagnosis. This paper also presents
a comparison among the learning functions (LM, GD and BR) where Levenberg-Marquardt based
learning function shows its efficiency over the others. Comparison between FES and NFS shows
the effectiveness of using NFS over FES. Here, the input data set has been collected from 10
patients whose ages are between 20 and 40 years, both for male and female. It has shown that the
neuro fuzzy system used in this study has the capacity to produce higher overall prediction
accuracy than particular fuzzy expert system architecture. Based on this observation authors
concluded that NFS represents a useful method for medical diagnostic task of finding
hypertension risk factor. Different ANN training algorithms were shown to lead to different
diagnostic results among which Levenberg- Marquardt is proved to be optimal. Development of
NFS would be more helpful to medical experts and new coming practitioner for diagnosing the
hypertension in proper order. For feed forward back propagation based neural network, authors
have taken the membership values of related linguistic variables (low, medium and high) as input
of age, BMI, BP and Heart Rate and as output (less, moderate and severe) for Hypertension risk
evaluation. In this present study, FES shows hypertension risk is moderate for 5 patients and less
for the other five patients. But NFS shows severe risk for 5 patients, less risk for 4 patients and
moderate risk for 1 patient which is close to medical observation as released by a team of experts.
Future goal of this study is to present the adaptive neuro fuzzy inference system (ANFIS) for
diagnosis of Hypertension and to make a comparative study with the existing system. More rules
and more symptoms might be added in the future research work for more precise diagnosis.
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Vida Groznika, Matej Guida, Aleksander Sadikova, Martin Mozinaa, Dejan Georgiev, Veronika
Kragelj, Samo Ribari, Zvezdan Pirtosek and Ivan Bratkoa [16] (2012) have authored the article
Artificial Intelligence in Medicine. This paper describes the use of expert’s knowledge in
practice and the efficiency of a recently developed technique called argument-based machine
learning (ABML) in the knowledge elicitation process. Authors are developing a neurological
decision support system to help the neurologists differentiate between three types of tremors:
Parkinsonian, essential and mixed tremor (comorbidity). The system is intended to act as a second
opinion for the neurologists and most importantly to help them reduce the number of patients in
the “gray area” that require a very costly further examination (DaTSCAN). Authors strived to
elicit comprehensible and medically meaningful knowledge in such a way that it does not come at
the cost of diagnostic accuracy. 122 patients were enrolled into the study. The classification
accuracy of the final model was 91%. Equally important, the initial and the final models were
also evaluated for their comprehensibility by the neurologists. All 13 rules of the final model
were deemed as appropriate to be able to support its decisions with good explanations. This paper
demonstrates ABML’s advantage in combining machine learning and expert knowledge. The
accuracy of the system is very high with respect to the current state-of-the-art in clinical practice
and the system’s knowledge base is assessed to be very consistent from a medical point of view.
Authors have also measured the net time involvement of the expert in building a knowledge base
for the system. Authors believe ABML saves a significant amount of expert’s time and the
experts agreed that the process itself felt very natural and stimulating.
Rajdeep Borgohain and Sugata Sanyal [17] (2012) have discussed the implementation of a rule
based expert system for diagnosing neuromuscular diseases. The proposed system is implemented
as a rule based expert system in JESS for the diagnosis of Cerebral Palsy, Multiple Sclerosis,
Muscular Dystrophy and Parkinson’s disease. In this system, the user is presented with a list of
questionnaires about the symptoms of the patients based on which the disease of the patient is
diagnosed and possible treatment is suggested. This system can aid and support the patients
suffering from neuromuscular diseases to get an idea of their disease and possible treatment for
the disease. Author’s presented an expert system for diagnosis of neuromuscular disorders, which
is used to diagnose some of the most common neuromuscular diseases i.e. Cerebral Palsy,
Muscular Dystrophy, Parkinson’s disease and Multiple Sclerosis. The system is a rule based
expert system implemented using the Java Expert System Shell using the backward chaining
mechanism. The expert system can go a great deal in supporting the decision making process of
medical professionals and also help patients with neuromuscular disorders and give an overview
of the disease and treatment options.
A.Sh.AMOOJI [18] (2013) has authored the article the application of expert systems in medical
diagnosis, which is very interesting and it creates considerable importance system of diagnosis.
The proposed system can help doctors and patients in providing decision support system,
interactive training tools and expert skills. The system constitutes part of intelligent system for
diagnosis of neurological diseases that used in one of the great hospital in Tehran. All of the
neurological diseases diagnoses have been investigated in this project. The system constitutes part
of intelligent system of diagnosis of neurological diseases. The present expert system is evolving
and increasing efficiency for all neurological diseases. Therefore the work was aimed to design a
system for the diagnosis of neurological diseases using FC (Fuzzy Cognitive) which is a
successful application of Lotfizadeh's fuzzy set theory. It is a reasonable tool for dealing with
uncertainty and imprecision and the knowledge of a physician can be modelled using an FC.
Usefulness and power of a FC depends on its knowledge base which consists of a data base and a
rule base. It is observed that the performance of a FC mainly depends on its rule base, and
optimizing the membership function distributions stored in the data base is a fine tuning process.
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The proposed work can be further improved and lengthened for the automation of disease (cancer,
heart disease, arthritis) prediction with the help of genetic algorithm and microarray gene
expression. Real data from health care organizations and agencies needs to be collected and all
the available techniques will be compared for the optimum accuracy.
Vahid Eslami, Sadreddin Rouhani-Esfahani, Nima Hafezi-Nejad, Farshid Refaeian, Siamak
Abdi, and Mansoureh Togha[19] (2013). A computerized expert system for diagnosing primary
headache based on International Classification of Headache Disorder. The authors developed a
computerized program designed to diagnose primary headache based on international
classification of headache disorders, criteria for use by physicians. An appropriate questionnaire
was designed according to the ICHD-II criteria for all types of primary headaches and the
computerized system provided diagnosis based on the criteria. The software was tested by
analyzing 80 patients, recruited from an outpatient headache clinic, affected by primary headache.
Each patient with a unique card number was interviewed up to 15 minutes. At the end of each
day, software and neurologist diagnoses were evaluated for each patient. Of 80 patients, the
software was able to come up with correct results in 78 cases. The age of the patients ranged from
30 to 80 years old. Migraine headache accounted for 71 cases, five patients had tension type
headache, and 2 had cluster headaches; all were correctly diagnosed by software. Two cases were
not concordant with the neurologist’s diagnosis. The neurologist diagnosed these two cases as
“Medical overuse syndrome headache” and “cluster headache”, which authors software was not
able to diagnosis them. This software permitted the diagnosis of more than 97% of the patients
similar to the physician's. Authors hope this questionnaire and applying the software to diagnose
headache based on ICHD could be of help to better the diagnosis of headaches.
Atul Krishan Sharma and Stuti Gupta [20] (2014) have developed Neurological Disorder
Diagnosis System. This paper presents an account of Rule-Based Expert System (RBES) for
Neurological Disorders, i.e., Alzheimer, Parkinson, Tetanus disease, Cerebral Palsy, Meningitis,
Epilepsy, Multiple Sclerosis, Stroke, Cluster headache, Migraine, Meningitis. Neurological
disorders are mainly concerned with the malfunctioning of nervous system. Detection and
monitoring of neurological disorders at early stage is essential for quality life and facilitate
necessary diagnosis and treatment of the diagnosed disease. The focus of this paper is the
development of Neurological Disorder Diagnosis System (NDDS), which can act as home agent
to detect the disorder with accuracy to that of an expert. The system consists of a knowledge base
with some facts. On the basis of these facts the medical practitioner will fed symptoms as input.
The system by applying inference procedures will return the output as results. More than 10 types
of neurological diseases can be diagnosed and treated by the system. In this paper, Neurological
Disorder Diagnosis System (NDDS) a rule based expert system is developed which helps in
diagnosing a nervous system disorder by analyzing the observed symptoms. This expert system is
developed to be used as a consultation system for neurologists and researchers in order to reach a
decision. The system developed is different from previously developed systems is in terms of
accuracy. The system is developed to be near possible as accurate as a human expert. This system
can be made advanced to deal with uncertainty using Fuzzy Based Reasoning Techniques. Fuzzy
logic provides high accuracy for problems based on uncertainty. The system can also be
developed as touch screen systems which can act as pocket systems to detect neurological
disorders.
Maíra Junkes-Cunha, Glauco Cardozo, Christine F Boos and Fernando de Azevedo [21] (2014)
have authored Implementation of expert systems to support the functional evaluation of stand-to-
sit activity background, functional evaluation of sit-to-stand and stand-to-sit activities was often
used by physiotherapists in patients with neurological and musculoskeletal disorders. The
observation of the way these activities are executed is essential in identifying kinesiological
problems.
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There are different methodologies used to describe the stand-to-sit activity and its evaluation is
not yet standardized, which makes the practical application of resources on clinical observation
difficult. The objective of this study is to automate the decision making process of an evaluation
protocol, developed in previous study and facilitate its utilization by professionals in the area.
The developed expert systems can support the physiotherapist in evaluating stand-to-sit activity
through a conclusion suggestion about the “level of inadequacy” for the “degree of inadequacy”
searched during its execution. Results of experts evaluation analyzed through statistical methods
indicate that the automation of protocols contributed to the standardization of the evaluation of
stand-to-sit activity and that it has application for teaching purposes.
Komal R. Hole and Prof.Vijay S.Gulhane Rule-Based Expert System for the Diagnosis of
Memory Loss Diseases [22] (2014).The paper presents a Rule-based Expert System for Memory
Loss Disease with the help of rules and facts. Also the Case-based approach is used for saving the
cases and for comparing the new case with previously saved cases. It will initially discuss
different approaches in designing of Medical Diagnosis Expert Systems with focus on all the
information about the memory loss. The different symptoms and causes of memory loss at
different age groups and the precautions for any kind of memory loss are covered. It is an attempt
to focus on some of very important diseases related to memory loss like Alzheimer’s disease,
Parkinson’s disease, Huntington’s disease, and dementia which are among the most common
types of memory loss diseases. The rule-based and case-based reasoning can be used for
designing diagnostic system. Case-based reasoning is often used where experts find it hard to
articulate their thought processes when solving problems. Expert system is a computer program
designed to model the ability of solving a problem by a human. In this dissertation an expert
system has been introduced to diagnose and suggest the treatments for the type of memory loss
diseases. Hence, first the purpose and goals of an expert system were defined and then the
relevant research reviewed. The case-based medical expert system prototype that supports
diagnosis of common diseases was developed. Several properties of this model remain to be to be
investigated. It should be tested on several more databases. Unfortunately databases are typically
proprietary and difficult to obtain. Future prospects for medical databases should be good since
some hospitals are now using computerized record system instead of traditional paper based. It
should be fairly easy to generate data for machine diagnosis.
3. CONCLUSIONS
This literature survey reveals that many researchers have applied soft computing techniques to
neurology problems. At the outset authors concluded that even after having significant research
in this field. The practical use of expert system by a neurologist in Indian hospitals is limited.
Authors would like to conduct an field survey of this aspect and would like to explore why the
use of research tools developed has not been/could not been applied in practice. This survey
would help us to understand the difficulties and limitations of such tools/software in Indian
context.
Authors would like develop an innovative solution which will suit our Indian neurologist doctors
requirements particularly remote and rural areas.
ACKNOWLEDGEMENTS
The authors would like to express gratitude to NMAM Institute of Technology, Nitte and
Basaveshwar Science College, Bagalkot for their support and funding present research. Authors