Machine Learning Models Predict Fatal Myocardial Infarction Within 10-Years Follow-Up Utilizing Explainable AI

• Published in: 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE) pp. 320 - 324
• Main Authors: Tsarapatsani, Konstantina-Helen, Sakellarios, Antonis, Tsakanikas, Vasilis D., Kleber, Marcus, Marz, Winfried, Fotiadis, Dimitrios I.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04-12-2023
• Subjects: explainable AI; machine learning; myocardial infarction; Myocardium; Predictive models; Receivers; Sensitivity; Support vector machines; Technological innovation; Testing
• ISSN: 2471-7819
• DOI: 10.1109/BIBE60311.2023.00059

Fatal myocardial infarction (MI) is one of the most common types of cardiovascular diseases that often presents in the emergency department. The prediction of death caused by myocardial infarction within 10-years follow-up is addressed in this study, using comorbidities, daily habits, clinical and laboratory data in binary and continuous data respectively. The used data are included in a cohort of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. The target feature, namely death caused by MI, contained 106 deceased patients and 2,321 alive patients in the final used dataset. The analysis was based on machine learning models (ML), such as support vector machine (SVM), light gradient-boosting machine (LGBM), Random Forest (RF), Decision Tree (DT). Their performance was estimated by Area Under Receiver Operating Characteristic Curve (AUC), Sensitivity, Specificity, Precision and Accuracy. Results show that LGBM was the most suitable of the aforementioned models to predict death caused by myocardial infarction within 10-years follow-up, achieving area under the curve (AUC) value equal to 77.03 %, accuracy 69.42 %, sensitivity 69.75 %, specificity 69.40% and precision 53.76 %. In addition, explainable artificial intelligence (xAI) was utilized and especially SHapley Additive exPlanations (SHAP) was the selected method. SHAP was utilized in order to shed light on the results, applying the LGBM model as the best predictive model. The provided SHAP plots contribute to the interpretation of how each independent feature aid to the final prediction.