Fine-Tuning Multilayer Perceptron Classifiers for Enhanced Heart Disease Prediction

Cardiovascular diseases (CVDs) represent a significant global health burden that takes millions of lives each year. This study evaluates the effectiveness of Multilayer Perceptron (MLP) classifiers in predicting CVD using the Kaggle Heart Disease Dataset. We implemented a structured approach using data preprocessing, model training with baseline MLP classifiers, and hyperparameter optimization utilizing Random and Grid Search techniques. Our evaluation employed accuracy, precision, recall, and F1-score. The optimized models, particularly the MLP Classifier trained with Grid Search, outperformed the baseline, achieving up to 98.54% accuracy and 100% precision. However, analysis of learning curves indicated the need for further model refinement to enhance generalization on unseen data. This study underscores the importance of hyperparameter tuning in developing effective MLP classifiers for CVDs prediction and suggests directions for future research, including exploring advanced optimization methods and regularization techniques.