Optimizing Heart Disease Risk Assessment Through Innovative Model Ensemble Techniques

Abstract

Heart disease remains an increasing world health problem, contributing to a significant share of morbidity and worldwide. Early detection and correct prediction of heart disease are needed for good treatment and preventative plans. In recent time, machine learning approaches, particularly ensemble learning, have demonstrated promise for increasing the accuracy of cardiac disease prediction models. This study investigates the usefulness of ensemble learning algorithms, such as Random Forest, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM), in predicting heart disease using clinical data. The study used a large dataset of 70000 patients that included demographic information, clinical assessments, and cardiovascular risk factors. Data pretreatment techniques are used to deal with missing values, outliers, and feature engineering. Ensemble learning models are trained.The findings show that ensemble learning is more successful in improving the prediction performance of heart disease models than individual algorithms. The Random Forest, MLP, and SVM models all perform well, and ensemble approaches improve accuracy and resilience even more. These findings demonstrate the potential of ensemble learning approaches to assist healthcare practitioners in early diagnosis and risk classification of heart disease patients. This discovery has implications for clinical practice, as precise prediction models may help with personalized treatment planning and resource allocation. Future research paths may include investigating different ensemble techniques, including domain knowledge, and integrating real-time data sources to enable continuous model development.