Hybrid Classification Approach for Heart Disease Prediction

Abstract

The term "data mining" describes a technology that aids in extracting significant data from a large amount of amorphous data. On the other side, predictive analysis uses current data to forecast future events. The focus of this initiative is on foreseeing heart organ morbidities. The data is pre-processed, characteristics are extracted, and classification are all done as part of this method of predicting heart disease. The application of LR and RF algorithms results in the building of a hybrid framework. While LR categorizes the features, the RF algorithm seeks to extract them. A range of criteria are used in this study to evaluate how competent the designed framework is. The method suggested has a 95% accuracy rate for predicting heart conditions.