Hybrid model for heart disease prediction using random forest and logistic regression

Abstract

Data mining is a method in which the valuable data is mined from the rough data. The futuristic outcomes are forecasted using recent information in the prediction analysis. The more useful, efficient, and commercial management of health resources after the recognition of risks, the prediction of disease in people or the prediction of hospital entry’s length is facilitated through it. This research work deals with the prediction of the heart disease. There are several steps that are included in the heart disease prediction. The preprocessing, feature selection and classification are some of these steps. The Random Forest (RF) and logistic regression based the hybrid scheme are introduced. The features are selected using RF. The implementation of Logistic Regression (LR) is done for classification. The analysis of performance of the recommended model for acquiring accuracy, precision, and recall is completed in this research. The accuracy has obtained in predicting the heart disease from this model is evaluated 95.08%.