Genetic algorithm-based approach to improve the prediction of myocardial infarction complications optimization of class imbalance ratio
Introduction: This article is the result of research conducted as part of a doctoral thesis in the Computer Science Ph.D. program at the University of Cauca in 2025. Myocardial infarction (MI) is a condition associated with a high mortality rate during the first year following onset, and life-threatening complications can arise within the first few hours after hospital admission. Therefore, early prediction of these complications improves prognosis and reduces risks.
Problem: Machine learning algorithms predict potential complications; however, class imbalance in the datasets skews the predictions toward the majority class. Objective: To improve the predictive performance of an ML model by rebalancing the class distribution in the training dataset using a data-driven approach.
Methodology: The CRISP-DM methodology was adapted.
Results: We propose a genetic algorithm (GA) to identify the optimal rebalancing ratio from the data. The best-performing individual determined the optimal rebalancing ratio through random oversampling (ROS) using an XGBoost model, which correctly classified 7 out of 8 cases (F1 score: 0.36), outperforming the baseline model, which correctly classified 6 out of 8 cases (F1 score: 0.28).
Conclusions: An approach that optimizes the class-balancing ratio improves the F1 score and detects an additional class compared to the base model without class balancing. Originality: This article proposes a mechanism to find the optimal proportion with which an imbalanced dataset should be rebalanced.
Limitations: Processing is required when working with larger datasets.
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