Den-SOFA: Dental Student Outcome Forecasting Assistant using explainable machine learning models

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are increasingly being explored in dental education to support academic assessment and identify students at risk of poor performance. However, predictive modeling in this setting remains challenging because complex, nonlinear relationships among academic and demographic variables influence student achievement. Moreover, the value of such models lies not only in prediction but also in interpretability. This study evaluated Den-SOFA, an explainable ML framework, as an exploratory proof-of-concept for predicting outcomes on restorative dentistry exit examinations. METHODS: This descriptive-analytical educational data-mining study analyzed data from 96 dental students. Twenty-six academic and demographic variables were included, such as cumulative GPA, theoretical and practical course grades, academic progression indicators, and demographic characteristics. The predictive performance of logistic regression, random forest, XGBoost, CatBoost, and artificial neural networks (ANN) was evaluated for binary (pass/fail) and multiclass (grade A-F) outcomes. Performance was assessed using accuracy, F1-score, AUC-ROC, MCC, sensitivity, and specificity. Model interpretability was examined using SHAP analysis. RESULTS: For pass/fail prediction, the ANN model achieved the highest discrimination (AUC-ROC = 0.906; accuracy = 0.86; MCC = 0.71), while the random forest showed comparable performance (AUC-ROC = 0.864; accuracy = 0.86) with greater interpretability. Multiclass grade prediction showed only moderate performance across models, with the best AUC-ROC of 0.775. SHAP analysis identified academic term, cumulative GPA, phantom laboratory performance, and theoretical course grades as the most influential predictors, whereas demographic variables contributed minimally to model performance. CONCLUSION: Den-SOFA provides an exploratory and hypothesis-generating framework for examining how explainable ML may be applied to dental education data. In this single-institution dataset, the models demonstrated moderate-to-good internal predictive performance, particularly for binary outcome prediction. External validation in larger and more diverse cohorts is required before any operational use in student support or assessment planning can be considered.