Prediction Heart Disease Using Naive Bayesian Classification

Abstract

Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it is based, rarely true in real-world applications. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not mined to discover of hidden information for effective decision making. Advanced data mining techniques can help medication. In this system, we developed a prototype that is Prediction Heart Disease Using Naive Bayesian Classification. We exploited medical profiles such as age, gender, blood pressure and blood sugar , it can predict the likelihood of patients getting a heart disease. This system is computer-based, user-friendly interface and the accuracy are reliable and expandable Moreover, we tested the train data of 326 and test data of 177 records and measured the performance with sensitivity and specificity So, the experimental result shows that the accuracy got 91.21%.