Biometrics and Bioinformatics

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Privacy Preserved Heart Disease Using SVM


Affiliations
1 Francis Xavier Engineering College, Tirunelveli, TamilNadu, India
     

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Data mining techniques have been applied magnificently in many fields including business, science, the Web, bioinformatics, and on different types of data such as textual, visual, spatial, real-time and sensor data. Medical data is still rich in information but poor in knowledge. There is a lack of effective analysis tools to discover the hidden relationships and trends in medical data obtained from clinical records. This paper reviews the state of the research in art on heart disease diagnosis and prediction. It present an overview of the current research being carried out using the data mining techniques to enhance heart disease diagnosis and prediction using Support Vector Machine (SVM). Results show that this algorithm perform positively high to predict the presence of Coronary Heart Diseases (CHD).

Keywords

Support Vector Machine, Coronary Heart Diseases.
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  • Privacy Preserved Heart Disease Using SVM

Abstract Views: 236  |  PDF Views: 4

Authors

D. Louisa Mary
Francis Xavier Engineering College, Tirunelveli, TamilNadu, India
S. Manimala
Francis Xavier Engineering College, Tirunelveli, TamilNadu, India

Abstract


Data mining techniques have been applied magnificently in many fields including business, science, the Web, bioinformatics, and on different types of data such as textual, visual, spatial, real-time and sensor data. Medical data is still rich in information but poor in knowledge. There is a lack of effective analysis tools to discover the hidden relationships and trends in medical data obtained from clinical records. This paper reviews the state of the research in art on heart disease diagnosis and prediction. It present an overview of the current research being carried out using the data mining techniques to enhance heart disease diagnosis and prediction using Support Vector Machine (SVM). Results show that this algorithm perform positively high to predict the presence of Coronary Heart Diseases (CHD).

Keywords


Support Vector Machine, Coronary Heart Diseases.

References