Heart Disease Prediction using Integrated Technology of XGBoost, Random Forest and Multi-Layer Perceptron

Abstract

Cardiovascular disease remains a leading cause of death worldwide, requiring prompt and accurate diagnosis to minimize patient mortality rates. More recent developments in artificial intelligence (AI) applications have demonstrated how to enhance prognostic performance and interpretability in clinical diagnosis. This research paper analyzes the application of machine and Deep Learning models for heart disease prediction by voting with a selection of models in order to develop a strong classifier. A weighted ensemble voting approach is employed and leverage is made from XGBoost, Random Forest, and Multi-Layer Perceptron (MLP) model strengths. Further, explainability is offered by SHapley Additive exPlanations (SHAP) to facilitate model decisions, allowing feature importance and decision-making insight. The proposed methodology is supported by established performance metrics, retaining clinical relevance. Results imply that AI-based approaches can achieve elevated predictive accuracy and interpretable diagnoses, informing the creation of automated cardiovascular risk stratification.