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Item PERFORMANCE ANALYSIS OF SOFT COMPUTING TECHNIQUES TOWARDS HEART DISEASE DIAGNOSIS SYSTEM(International Journal of Computer Science and Mobile Computing, 2017-12) S, NithyaData mining is the extraction of concealed prescient data from expansive databases furthermore a capable of new innovation with incredible potential to examine critical data in their data warehouses. Data mining algorithms anticipate future patterns and behaviors, permitting organizations to make proactive and knowledge driven choices. The computerized, forthcoming analyses offered by data mining move beyond investigations of past occasions gave by review tools commonplace of choice emotionally supportive networks. Data mining algorithms can answer business addresses that customarily were excessively prolonged to determine. They scour databases for concealed examples, discovering the prediction of disease that specialists may miss in light of the fact that it lies outside their desires. Manual checking is highly impossible to diagnose for this disease. To predict heart disease several approaches have been carried out. This comparative study paper provides a thorough analysis of various algorithms made towards disease prediction. Several data mining and soft computing approaches are studied. This study concludes that the performance of various algorithms comparison of accuracy, sensitivity and specificity of several algorithms and approaches.Item FUZZY LOGIC BASED IMPROVED SUPPORT VECTOR MACHINE (FISVM) CLASSIFIERFOR HEART DISEASE CLASSIFICATION(ARPN Journal of Engineering and Applied Sciences, 2015-09) S, Nithya; C, Suresh Gnana DhasClassification is the major research topic in data mining. Typically classification represents the data to be categorized based on its features or characteristics. This proposed research work aims in developing fuzzy logic based improved support vector machine classifier. Support vector machine is a type of supervised machine learning technique and once when the dataset is given as input it performs the classification task by itself. The proposed classifier aims in improving the classification accuracy of the support vector machine by making use of fuzzy logic. The proposed classifier has been tested on two different datasets namely PIMA Indian diabetes dataset and Z-AlizadehSani dataset in order to classify the occurrence of heart disease among the patients. Performance metrics sensitivity, specificity and classification accuracy are taken for comparison of the proposed fuzzy logic based improved support vector machine classifier (F-ISVM) with several classification algorithms. Results showed that the proposed F-ISVM classifier gives better classification accuracy than that of support vector machine, naive bayes, neural networks, sequential minimal optimization (SMO) and bagging SMO classifiers.Item AN IMPROVED SUPPORT VECTOR MACHINE (I-SVM) CLASSIFIER FOR HEART DISEASE CLASSIFICATION(International Journal of Applied Engineering Research, 2015) S, Nithya; C, Suresh Gnana DhasClassification is the major research issue in data mining. Usually classification represents the data to be categorized based on its features or characteristics. This research work aims in developing an improved support vector machine classifier. Support vector machine is a type of supervised machine learning technique and once when the dataset is given as input it performs the classification task by itself. The proposed classifier aims in improving the classification accuracy of the support vector machine. The proposed classifier has been tested on two different datasets namely PIMA Indian diabetes dataset and Z-Alizadeh Sani dataset in order to classify the occurrence of heart disease among the patients. Performance metrics sensitivity, specificity and classification accuracy are taken for comparison of the proposed improved support vector machine classifier (I-SVM) with several classification algorithms. Results showed that the proposed classifier gives better classification accuracy than that of support vector machine, naive bayes, neural networks, sequential minimal optimization (SMO) and bagging SMO classifiers.Item CORONARY ARTERY DISEASE (CAD) PREDICITON AND CLASSIFICATION – A SURVEY(International Journal of Applied Engineering Research, 2015) S, Nithya; C, Suresh Gnana DhasData mining is the extraction of concealed prescient data from expansive databases furthermore a capable of new innovation with incredible potential to examine critical data in their data warehouses. Data mining algorithms anticipate future patterns and behaviors, permitting organizations to make proactive and knowledge driven choices. The computerized, forthcoming analyses offered by data mining move beyond investigations of past occasions gave by review tools commonplace of choice emotionally supportive networks. Data mining algorithms can answer business addresses that customarily were excessively prolonged to determine. They scour databases for concealed examples, discovering the prediction of disease that specialists may miss in light of the fact that it lies outside their desires. Manual checking is highly impossible to diagnose for this disease. To predict heart disease several approaches have been carried out. This comparative study paper provides a thorough analysis of various algorithms made towards heart disease prediction. Several data mining and soft computing approaches are studied. This study concludes that the performance of various algorithms comparison of accuracy, sensitivity and specificity of several algorithms and approaches.Item CORONARY ARTERY DISEAE (CAD) PREDICTION AND CLASSIFICATION(Vivekanandha College of Engineering For Women, 2015-03-11) S, NithyaData mining is the extraction of concealed prescient data from expansive databases furthermore a capable of new innovation with incredible potential to examine critical data in their data warehouses. Data mining algorithms anticipate future patterns and behaviors, permitting organizations to make proactive and knowledge driven choices. The computerized, forthcoming analyses offered by data mining move beyond investigations of past occasions gave by review tools common place of choice emotionally supportive networks. Data mining algorithms can answer business addresses that customarily were excessively prolonged to determine. They scour databases for concealed examples, discovering the prediction of disease that specialists may miss in light of the fact that it lies outside their desires. Manual checking is highly impossible to diagnose for this disease. To predict heart disease several approaches have been carried out. This comparative study paper provides a thorough analysis of various algorithms made towards heart disease prediction. Several data mining and soft computing approaches arestudied. This study concludes that the performance of various algorithms comparison of accuracy, sensitivity and specificity of several algorithms and approaches.