Optimizing heart disease diagnosis with advanced machine learning models: a comparison of predictive performance

Cardiovascular disease is the leading cause of mortality globally, necessitating precise and prompt predictive instruments to enhance patient outcomes. In recent years, machine learning methodologies have demonstrated significant potential in enhancing the precision and efficacy of health-related predictions, especially in the identification of heart disease. The dataset used in this study came from the UC Irvine Machine Learning Repository and included data from Cleveland, Switzerland, Hungary, Long Beach, and Statlog. We selected seven of the 1,190 cases, each with 12 attributes, for analysis. We used different machine learning models, like Random Forest, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Gradient Boosting, AdaBoost, XGBoost, and Bagged Trees, to check performance using accuracy, precision, recall, F1-score, and ROC-AUC. K-fold cross-validation (K = 10, K = 5) was conducted to guarantee the robustness and generalizability of these models. Random Forest exhibited remarkable stability, attaining 94% accuracy with K = 10 and 92% with K = 5, whereas XGBoost had a minor decrease during cross-validation (90% for K = 10, 89% for K = 5). KNN demonstrated possible overfitting, evidenced by a notable decline in accuracy (71% for K = 10, 72% for K = 5). XGBoost and Bagged Trees achieved the highest accuracy of 93%, followed by Random Forest and KNN at 91%. Furthermore, Random Forest and Bagged Trees exhibited the highest ROC-AUC values at 95%, and XGBoost demonstrated a ROC-AUC of 94%. The results demonstrate the effectiveness of ensemble methods in predicting cardiac diseases, along with the potential for future advancement through the incorporation of hybrid models and advanced survival analysis techniques.