Special Issue Article Open Access

Heart Disease Diagnosis Using Predictive Data mining

Abstract

Heart disease is a major health problem and it affects a large number of people. Cardiovascular Disease (CVD) is one such threat. Unless detected and treated at an early stage it will lead to illness and causes death. There is no adequate research focus on effective analysis tools to discover relationships and trends in data especially in the medical sector. Health care industry today generates large amounts of complex clinical data about patients and other hospital resources. Data mining techniques are used to analyze this rich collection of data from different perspectives and deriving useful information. This project intends to design and develop diagnosis and prediction system for heart diseases based on predictive mining. Number of experiments has been conducted to compare the performance of various predictive data mining techniques including Decision tree and Naïve Bayes algorithms. In this proposed work, a 13 attribute structured clinical database from UCI Machine Learning Repository has been used as a source data. Decision tree and Naive Bayes have been applied and their performance on diagnosis has been compared. Naive Bayes outperforms when compared to Decision tree.

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