Abstract
Aim: We developed a machine learning model using EuroScore assumptions and preoperative and intraoperative risk factors to predict mortality after coronary artery bypass graft (CABG). Materials & methods: We retrospectively examined data from 108 CABG patients at King Abdullah University Hospital, classifying them into risk groups via EuroScore and predicting mortality through random forest classification. Results: High-risk patients displayed longer surgical times and significant factors such as age and surgery choice. The median EuroScore was 0.95 (0.5-6.4). The model yielded high AUC scores (0.98, 0.95) indicating strong predictive accuracy. Conclusion: Our findings showed that the machine learning models combined with the EuroScore significantly improve post-CABG mortality prediction. For further validation, larger datasets are needed.
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