ArticleBMC medical education2024
Predicting students' academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes.
Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 24 citations in OpenAlex.
- Medical laboratory science course performance and grade point average as indicators of ASCP certification success: a 10-year review from an NAACLS-accredited program.Laboratory medicine · 2026Article
- Enhancing graduate education assessment: a machine learning-based classification of academic performance in medical students.BMC medical education · 2026Article
- Admission Routes and Demographics as Predictors of Academic Performance in Medical Students: A Retrospective Cohort of Grade Point Averages (GPAs) and Comprehensive Exam Scores.Advances in medical education and practice · 2026Article
- Early detection of at-risk health sciences students: a machine learning-based predictive study using midterm grades.BMC medical education · 2025Article
- Examining the empathy levels of medical students using CHAID analysis.BMC medical education · 2025Article
- Article
- The impact of mindfulness on academic achievement of students with the mediating role of adaptability: a structural equation modeling approach.BMC medical education · 2024Article
- Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units.Frontiers in public health · 2024Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDropout 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. The main goal of this study was to use machine learning models, Artificial Neural Networks (ANN) and Naïve Bayes (NB), to identify first year medical students that succeed academically, using sociodemographic data and academic history.
methodsData from seven cohorts (2011 to 2017) of admitted medical students to the National Autonomous University of Mexico (UNAM) Faculty of Medicine in Mexico City were analysed. Data from 7,976 students (2011 to 2017 cohorts) of the program were included. Information from admission diagnostic exam results, academic history, sociodemographic characteristics and family environment was used. The main dataset included 48 variables. The study followed the general knowledge discovery process: pre-processing, data analysis, and validation. Artificial Neural Networks (ANN) and Naïve Bayes (NB) models were used for data mining analysis.
resultsANNs models had slightly better performance in accuracy, sensitivity, and specificity. Both models had better sensitivity when classifying regular students and better specificity when classifying irregular students. Of the 25 variables with highest predictive value in the Naïve Bayes model, percentage of correct answers in the diagnostic exam was the best variable.
conclusionsBoth ANN and Naïve Bayes methods can be useful for predicting medical students' academic achievement in an undergraduate program, based on information of their prior knowledge and socio-demographic factors. Although ANN offered slightly superior results, Naïve Bayes made it possible to obtain an in-depth analysis of how the different variables influenced the model. The use of educational data mining techniques and machine learning classification techniques have potential in medical education.
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