Comparison of machine learning classification and regression models for prediction of academic performance among postgraduate public health students

Abstract

Abstract Machine learning (ML) is an artificial intelligence tool that focuses on learning by generating models using established algorithms that represent a given dataset. It can be used as a predictive tool for students’ academic performance (AP) at both undergraduate and postgraduate levels. A cross-sectional analysis was conducted using academic records of 922 postgraduate students admitted to the High Institute of Public Health, Alexandria University, Egypt, between 2020–2024. Data included 22 features spanning pre-enrollment metrics, academic performance, and demographic traits. Classification algorithms, and regression models were trained on 75% of the dataset, validated via 5-fold cross-validation. Performance metrics included accuracy, precision, recall, AUC for classification, and MAE, RMSE, and R² for regression. Regression models outperformed classification models in AP prediction, with Ensemble (Soft Voting) achieving the highest accuracy (74.25%), lowest MAE (0.3383), and RMSE (0.4316). Among classification models, Random Forest demonstrated superior accuracy (71.43%) and AUC (0.87). Numerical features like the number of failed courses showed the strongest negative correlation with AP ( r = -0.37). Key predictors included bachelor’s university, major, department, and pre-enrollment CGPA. Feature importance analysis highlighted failed courses as the top determinant, followed by institutional and academic background variables. Regression-based ML models, particularly Ensemble (Soft Voting), proved more effective than classification approaches for predicting nuanced variations in AP. These findings enable institutions to prioritize early interventions for at-risk students, and optimize resource allocation. However, moderate R² values (0.3832) underscore the need to integrate psychosocial and behavioral factors in future studies.