Enhancing Heart Disease Prediction Accuracy through a Novel Hybrid Machine Learning Model

Heart disease continues to be one of the leading causes of death globally, resulting in millions of fatalities each year. Despite substantial progress in medical science, the early detection and accurate prediction of heart disease remain significant challenges. Traditional diagnostic methods, though effective, often fail to identify at-risk individuals early enough, leading to delayed treatment and higher mortality rates.This research paper introduces a novel hybrid machine learning model aimed at enhancing the accuracy of heart disease prediction. By integrating various machine learning techniques, the proposed model harnesses their complementary strengths to provide a robust and reliable tool for early diagnosis. The study assesses the performance of the hybrid model against traditional singlemodel approaches using a comprehensive dataset. The proposed method achieved an accuracy of 95.5%, a mean absolute error (MAE) of 0.403, and a root mean square error (RMSE) of 0.203, demonstrating its potential to significantly improve prediction accuracy.The paper presents a detailed analysis of the proposed hybrid model, including its design, implementation, and evaluation. The results indicate that the hybrid approach not only surpasses individual models but also offers a scalable solution for clinical applications. The implications of this research are farreaching, as it can improve patient outcomes through timely and accurate diagnosis, ultimately contributing to better healthcare management and reduced healthcare costs.