Early detection of at-risk health sciences students: a machine learning-based predictive study using midterm grades

BACKGROUND: Early identification of students at academic risk is critical in health sciences education, particularly in regions prioritizing healthcare workforce development. This study evaluated the application of established machine learning (ML) classifiers as a regional case study in the United Arab Emirates (UAE), using first-term midterm performance as early, formative assessment indicators of academic integration and student persistence. METHODS: Academic records from 346 first-year students across seven programs were analyzed. All students were enrolled in three common courses: Biology, English for Medical Sciences, and Introduction to Health Sciences. Five supervised ML models (XGBoost, Random Forest, Logistic Regression, Gradient Boosting Machine (GBM), and Naïve Bayes) were trained using Random Over-Sampling Examples (ROSE) with five-fold cross-validation. Model performance was assessed with F1-scores and area under the curve (AUC). Interpretability was examined via variable-importance plots and Shapley Additive exPlanations (SHAP) for XGBoost. RESULTS: XGBoost and Logistic Regression showed the highest performance (F1 = 0.84; AUC > 0.91). Midterm scores in Biology and Introduction to Health Sciences were consistently the strongest predictors, and SHAP analyses indicated they had the largest case-level impact on risk predictions. CONCLUSIONS: Findings demonstrate the feasibility of using midterm grades as early, pedagogically meaningful indicators to identify at-risk students in a UAE health sciences context. While limited in scope to one cohort and institution, the study provides regionally relevant evidence to support timely academic interventions. Future research should include behavioral and psychosocial predictors and validate across multiple cohorts and institutions. TRIAL REGISTRATION: Not applicable.