Heart Disease Prediction

Rohit Choudhary , Mohd. Asif Khan, Ashish Parasar

One of the major cause of death is heart attack disease, anywhere across the globe. In healthcare system, data mining play a vital role to qualify health systems to properly use the data and analytics to identify impotence that improves care with reduce costs and in less services. Classification is one of data mining technique as supervised technique employed to accurately predict the disease type in each case in the heart attacks. Heart attacks classification involves identifying healthy and sick personalities. Naive Bayes (NB) works as linear classifier that relatively stable regarding small variation or changes in training data at the function level. In order to collect relevant features, an efficient evolutionary computation technique like Particle Swarm Optimization (PSO) employed and contributes more to the result which diminishes the computation cost and increases the precision and efficiency. The numerical result shows that the PSO increases the classification accuracy with the help of NB classifier as fitness function to accurately classify disease.