A Comparative Study of Ensemble Learning and Neural Networks for the Heart Disease Prediction

Heart disease continues to be a leading global cause of death, making the development of predictive models for early diagnosis a critical task. This study investigates the performance of various machine learning and deep learning models for heart disease prediction using a structured dataset of 918 observations and 11 features. The analysis includes ensemble methods like Random Forest, Gradient Boosting, and XGBoost, as well as neural networks such as Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). Traditional classifiers, including Support Vector Machines (SVM) and Logistic Regression, are also considered for benchmarking. The dataset was preprocessed using label encoding, standardization, and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and ensure data consistency. Model evaluation was conducted using key metrics such as precision, recall, F1-score, and ROC-AUC. The results demonstrated that ensemble methods, particularly Random Forest (ROC-AUC: 0.9313) and Gradient Boosting (ROC-AUC: 0.9279), consistently delivered superior performance. Among neural networks, MLPs showed promising results (ROC-AUC: 0.9232), outperforming CNNs, which were less effective in handling tabular data. Meanwhile, TabNet was found to be unsuitable for this dataset, as it significantly underperformed across all metrics. This research highlights the effectiveness of ensemble methods and MLPs in heart disease prediction and the importance of proper preprocessing techniques. Future work could focus on integrating hybrid models or advanced optimization techniques to further enhance predictive accuracy in clinical settings.