Advanced Heart Attack Risk Prediction Using Stacked Hybrid Machine Learning

In this paper, an all-inclusive machine learning framework is developed for predicting the risk of heart disease by using many advanced classification techniques. Heart disease have been one of the leading causes of death worldwide, and early detection forms the basis of effective intervention and treatment. We implement and compare hybrid models that combine algorithms like Random Forest, Support Vector Machine, XGBoost, and logistic regression for predictive performance improvement. Besides, we used some traditional models, like K-Nearest Neighbors, Naive Bayes, and Decision Trees, as baseline comparisons. This work suggests how feature importance analysis using Random Forest is a critical step towards identifying key predictors in presence of heart disease.