Heart Disease Diagnosis Using Hybrid Machine Learning Model in Layered Classification Approach

Tarun Kumar Agrawal, Dharmendra Gupta

Abstract: In recent decades, the incidence of heart disease has increased due to increasingly stressful lives and careless human behaviour, making it one of the major causes of mortality globally. Accurate and prompt prediction of heart disease is critical to successful prevention. Many techniques have been developed to help healthcare practitioners forecast this illness, but each algorithm has its own set of limitations. This research introduces a novel ensemble approach that improves forecast accuracy while lowering false alarms. The suggested technique was evaluated against both classic and new algorithms, and the findings show a considerable improvement in prediction accuracy.

Keywords: AHA, HRV, IHDPS, SVM, DT, NB, K-NN, MLP, CFS, BFS