A data balancing approach towards design of an expert system for Heart Disease Prediction

TL;DR

This paper explores machine learning techniques, especially ensemble methods like Random Forest, combined with feature selection and oversampling, to improve the accuracy of heart disease prediction models.

Contribution

It introduces a comprehensive approach integrating multiple ML models, feature selection techniques, and oversampling to enhance heart disease prediction accuracy.

Findings

Random Forest achieved 99.83% accuracy.

Ensemble methods outperform individual classifiers.

Key predictors include smoking, blood pressure, cholesterol, and inactivity.

Abstract

Heart disease is a serious global health issue that claims millions of lives every year. Early detection and precise prediction are critical to the prevention and successful treatment of heart related issues. A lot of research utilizes machine learning (ML) models to forecast cardiac disease and obtain early detection. In order to do predictive analysis on "Heart disease health indicators " dataset. We employed five machine learning methods in this paper: Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis, Extra Tree Classifier, and AdaBoost. The model is further examined using various feature selection (FS) techniques. To enhance the baseline model, we have separately applied four FS techniques: Sequential Forward FS, Sequential Backward FS, Correlation Matrix, and Chi2. Lastly, K means SMOTE oversampling is applied to the models to enable additional analysis. The findings show that when it came to predicting heart disease, ensemble approaches in particular, random forests performed better than individual classifiers. The presence of smoking, blood pressure, cholesterol, and physical inactivity were among the major predictors that were found. The accuracy of the Random Forest and Decision Tree model was 99.83%. This paper demonstrates how machine learning models can improve the accuracy of heart disease prediction, especially when using ensemble methodologies. The models provide a more accurate risk assessment than traditional methods since they incorporate a large number of factors and complex algorithms.