PERFORMANCE ANALYSIS OF CLASSIFICATION DATA MINING TECHNIQUES OVER HEART DISEASE DATA BASE

The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This paper describes about a prototype using data mining techniques, namely Naïve Bayes and WAC (weighted associative classifier).This system can answer complex “what if” queries which traditional decision support systems cannot. Using medical profile0073 such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. It can serve a training tool to train nurses and medical students to diagnose patients with heart disease. It is a web based user friendly system and can be used in hospitals if they have a data ware house for their hospital. Presently we are analyzing the performances of the two classification data mining techniques by using various performance measures.

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