1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan
2Department of Medical Laboratory Science and Biotechnology, Taipei Medical University, Taiwan
3School of Medicine and Allied Health Sciences, University of the Gambia, West Africa
*Corresponding author:Musa Touray, School of Medicine and Allied Health Sciences, University of the Gambia, MDI Road, Kanifing P.O. Box 3530, Serrekunda, West Africa
Submission:February 15, 2024; February 29, 2024
ISSN: 2578-0271Volume4 Issue5
Background: Cardiovascular diseases, particularly Coronary Artery Disease (CAD), remain the leading
cause of death worldwide, imposing significant health and economic burdens. It is crucial to emphasize
early diagnosis of CAD to prevent complications and improve patient outcomes. This study aims to
predict the likelihood of CAD recurrence within 6 months post-treatment.
Methods: The Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform
a retrospective study. Predictive features include demographic data and laboratory test results. A
6-month CAD recurrence was set as the study outcome. We used the Machine Learning (ML) Methods
Of Logistic Regression (LR), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to develop
a predictive model for CAD recurrence. The prognostic capacity and clinical utility of these three models
were compared using the Area Under the Receiver Operating Characteristic Curves (AUROC), precision,
sensitivity, specificity, f1 measure and Area Under Precision -Recall (AUPR) curve.
Results: Of 7,583 CAD patients in this study population, 2,361 (31%) had CAD recurrence during
6-month follow-up. Out of 38 features selected and extracted from the MIMIC III database, 15 variables
were chosen using stepwise regression. The RF model performed best with an AUC of 0.83. The top 6
significant features in our model were platelet, WBC, RBC, INR, chloride, and creatinine.
Conclusion: Our study shows that the random forest model outperforms the XGBoost and LR models in
predicting CAD recurrence within 6 months post-treatment. The study suggests a connection between
certain lab indices (platelet count, WBC, RBC, INR, chloride, calcium, creatinine) and CAD recurrence,
bridging knowledge gaps and guiding future research on preventive strategies and treatments for CAD.
Keywords: Prediction; Machine learning; Coronary artery disease; Recurrence; MIMIC-III database