Prediction of Myocardial Infarction Complications using Gradient Boosting.

Mohamed Khamis, Gamal Saad; Mohammed, Zakariya M. S.; Alanazi, Sultan Munadi; Mahmoud, Ashraf F. A.; Abdalla, Faroug A.; Bkheet, Sana Abdelaziz

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, representing a significant public health challenge. Myocardial Infarction (MI), a severe manifestation of CVDs, contributes substantially to these fatalities. Machine learning holds great promise for predicting MI. This study explores the potential of Gradient Boosting (GB) techniques for this purpose, explicitly focusing on CatBoost, LightGBM, XGBoost, and XGBoost Random Forest. The study leverages GB's embedded feature selection, missingvalue handling, and hyperparameter tuning capabilities. Performance was evaluated using multiple metrics: Area Under the Curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews Correlation Coefficient (MCC). A probabilistic comparison matrix was used to assess the relative performance of the GB models. The results demonstrate the superiority of CatBoost, achieving a classification accuracy of 94.9%, an AUC of 0.992, a recall of 94.9%, and an MCC of 0.82. The probabilistic comparison further confirms CatBoost's superior performance. These findings contribute to MI prediction, highlighting the predictive potential of the CatBoost algorithm and ultimately aiding the fight against MI to achieve better patient outcomes.

MYOCARDIAL infarction; FEATURE selection; MACHINE learning; RANDOM forest algorithms; STATISTICAL correlation

Engineering, Technology & Applied Science Research, 2024, Vol 14, Issue 6, p18550

2241-4487

Academic Journal

10.48084/etasr.9076