Hybrid Machine Learning Approaches for Robust Heart Disease Prediction: A Comprehensive Analysis

In addressing the global increase in prevalence of heart disease and its consequences for public wellness, this research emphasizes the need to have reliable prediction models. It is critical to promptly identify those who are at risk of cardiovascular disease to put preventive measures and appropriate treatments into place. In line with the World Health Organization (WHO), cardiovascular illnesses are responsible for a significant 31% of the world's mortality rate and cause an alarming 17.9 million deaths annually. Accurately assessing the severity of cardiac problems and forecasting their course is difficult due to the complicated clinical landscape, which is marked by intrinsic unpredictability and a complex interaction of components. As such, early detection becomes essential to the effective management of heart-related conditions. This study offers a thorough structure for forecasting cardiovascular illnesses by utilizing cutting-edge boosting strategies and machine learning approaches, such as Ada boost, Gradient boosting, CAT Boost, LG Boost, XG Boost and our proposed model together with GridSearchCV. With an emphasis on Early Heart Disease Prediction using Boosting-based ensemble Machine Learning Techniques, the motive of this research is to advance the creation of reliable models that can accurately predict the risks associated with cardiovascular health. Our proposed model with GridSearchCV is the best performer, according to the empirical results, with an accuracy of 99.8%. A comparative framework is given for all the methodologies. These findings highlight our model over competing algorithms with regard to their overall efficiency and anticipated accuracy, and they provide important new information for the field of cardiovascular health prediction.