IRJET - Effective Heart Disease Prediction using Distinct Machine Learning Techniques

This document discusses using machine learning techniques to predict heart disease. It proposes a new hybrid method combining machine learning techniques to improve prediction accuracy. Specifically, it introduces a prediction model using different feature combinations and classification techniques, including a hybrid random forest with linear model (HRFLM) that achieves 88.7% accuracy. Previous studies predicting heart disease using machine learning are also discussed, but the authors aim to develop a unique method focusing on feature selection to further improve prediction accuracy of heart disease.