International Journals

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/178

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    ANALYSIS OF VARIOUS DATA MINING TECHNIQUES TO PREDICT DIABETES MELLITUS
    (Research India Publications, 2016-01) J, Maria Shyla; M, Renuka Devi
    Data mining approach helps to diagnose patient’s diseases. Diabetes Mellitus is a chronic disease to affect various organs of the human body. Early prediction can save human life and can take control over the diseases. This paper explores the early prediction of diabetes using various data mining techniques. The dataset has taken 768 instances from PIMA Indian Dataset to determine the accuracy of the data mining techniques in prediction. The analysis proves that Modified J48 Classifier provide the highest accuracy than other techniques.
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    GENETIC BASED SUPPORT VECTOR MACHINE CLASSIFIER FOR HEART DISEASE CLASSIFICATION
    (Journal of Emerging Technologies and Innovative Research (JETIR), 2019-06) Nithya S
    Classification is one among the hot research topic in the field of data mining. Classically classification task represents the data to be categorized based on its features or characteristics. This proposed research work aims in developing genetic based 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 genetic algorithm. Genetic algorithm is used in order to perform fuzzy association rule extraction, candidate rule pre-screening, rule selection and lateral tuning. The proposed classifier has been tested onnamely PIMA Indian diabetes to classify the occurrence of heart disease among the patients. Performance metrics classification accuracy are taken for comparison of the proposed genetic based support vector machine classifier (GSVM) with SVM classification algorithm. Results showed that the proposed GSVM classifier gives better classification accuracy than that of support vector machine
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    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 Dhas
    Classification 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.