Predicting students' academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes.

Background: Dropout and poor academic performance are persistent problems in medical schools in emerging economies. Identifying at-risk students early and knowing the factors that contribute to their success would be useful for designing educational interventions. Educational Data Mining (EDM) methods can identify students at risk of poor academic progress and dropping out.