Hamadan University of Medical Sciences Journal of Research in Health Sciences 2228-7795 26 3 2026 08 15 A Predictive Model for Post-Percutaneous Coronary Intervention Readmission: Insights from Machine Learning on Clinical Risk Factors e00692 e00692 10.34172/jrhs.13875 EN Maryam Farhadian https://orcid.org/0000-0002-6054-9850 Seyed Kianoosh Hosseini https://orcid.org/0000-0001-6265-5932 Tahereh Roostami Journal Article 10.34172/jrhs.13875 2026 01 06 2026 06 09 Introduction: Unplanned one-year readmission after percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI) poses serious clinical and economic challenges. This study developed and validated a random forest (RF) model to predict one-year all-cause unplanned readmission in STEMI-PCI patients and identify key risk factors. Study Design: A single-center retrospective cohort study. Methods: This single-center retrospective cohort study was conducted at Farshchian Heart Hospital from September 2021 to September 2024. Overall, 1,836 STEMI patients undergoing primary PCI were analyzed to predict 365-day unplanned readmission. The primary outcome was 365-day unplanned readmission (binary: readmitted or not). A RF classifier was developed on an 80/20 training-test split, optimized through repeated cross-validation with SMOTE for class imbalance. A SMOTE-RF model was also employed to mitigate the significant class imbalance inherent in the 17.6% readmission rate. Finally, key predictors were identified using mean decrease in Gini impurity. Results: Operating at an optimized threshold, the RF model demonstrated moderate discriminative ability (AUC: 0.765) with a sensitivity of 62.5% and specificity of 78.5%. Moreover, the RF model outperformed logistic regression in terms of AUC (0.695 vs. 0.765) and specificity (78.5% vs. 52%), although logistic regression achieved higher sensitivity (78.5% vs. 62.5%). Multivessel disease, smoking, length of stay, hypertension, ejection fraction, and age were key predictors of readmission identified by the RF. Conclusion: The RF model exhibited acceptable predictive performance for one-year hospital readmission, enabling clinically interpretable risk stratification and identifying key clinical risk factors to inform tailored post-discharge care. However, external validation is required to confirm its generalizability and clinical applicability.