Machine Learning Ensemble Model for Heart Disease Prediction

One of the most pressing problems in world health today is the lack of accurate prediction models that can help with the early diagnosis and rapid treatment of cardiovascular disease. The primary focus of this study is to use machine learning techniques for the prediction of certain forms of cardiovascular illness, including Other Cardiovascular illness, Stable Angina, Coronary Artery Disease, and Unstable Angina. Age, sex, kind of chest pain, resting blood pressure, serum cholesterol, fasting blood sugar, and other important clinical diagnostic factors are included in the dataset. Current models that achieve 80% prediction accuracy include Logistic Regression and Naive Bayes. Unfortunately, they can't handle complex patterns in data and rely on linear assumptions, which limits their effectiveness. To enhance the precision of predictions, we advocate for the use of cutting-edge ML methods such as Random Forest and Gradient Boosting. Complex feature-nonlinear connection interactions are no match for these algorithms. Accuracy rates of 93% and 95%, respectively, have been achieved by these models by the use of their skills to tighten decision bounds and minimize errors via iterative learning. This research shows that these models have a chance to outperform the current system, providing clinicians with a reliable tool for better cardiac ailment classification, which would enhance healthcare choices and patient outcomes.