Prediction of heart disease using hybrid technique for selecting features

Generally Healthcare industry is known to be `information rich', but woefully all the data required to discover hidden patterns are not mined. For effective decision making in field of medical, advanced techniques of data mining are used. This paper proposed a prediction of heart disease using random forest and naive bayes. In addition, approach is proposed to select features before classification in order to improve performance of models. For feature selection, SVM-RFE and gain ratio algorithms are applied to dataset which in results assigns weight to each feature. This approach helps to improve accuracy and reduce computational time. Experimental results shows that proposed approach of selecting feature increases accuracy for both models.