Predicting Heart Attacks Using Machine Learning Models: A Comprehensive Approach

This study utilizes Decision Tree and Multilayer Perceptron (MLP) machine learning models to predict heart attack likelihood using a large, diverse dataset. By optimizing hyperparameters and leveraging significant features, the models achieved an accuracy and F1-score of 92.33%. The performance outperforms similar studies, showcasing the potential of machine learning in early heart disease diagnosis. Future work aims to apply optimization algorithms and validate the models with real-time clinical data.