Predicting Graduation in Undergraduate Medical Education: A Machine Learning Analysis Across Diverse High School Curricula

ABSTRACT

Background The United Arab Emirates (UAE) is characterised by a diverse educational landscape, where students enter medical school from various high school curricula. Understanding how these varied academic backgrounds influence medical students’ academic performance is essential. The transition to medical school is a critical phase, with graduation outcomes carrying important implications for both students and institutions. Identifying early predictors of success is crucial to improving student support and academic outcomes in undergraduate medical education.

Aim This study aimed to evaluate the predictive value of high school curriculum type on graduation outcomes in an undergraduate medical education program.

Methods A retrospective cohort study was conducted on undergraduate medical students enrolled at Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai Health, Dubai, UAE, from its inception in 2016 through 2024. The data were accessed for this research on 04/06/2024. The study employed machine learning methods, including Bayesian Networks (BN), Neural Networks (NN), and Random Forests (RF), to evaluate the predictive power of high school curriculum type and other academic variables for graduation success.

Results The study included 661 undergraduate medical students, predominantly female, 76.7% (n=507). Students represented 11 high school curricula, with the American (48.1%) and British (22.7%) systems being the most common. Among 122 students eligible to graduate, the Bayesian Network model demonstrated the highest predictive accuracy (AUC = 0.94). The cumulative GPA was the most influential predictor. The model correctly identified 269 out of 494 students (54.5%) as likely to graduate.

Conclusion The type of high school curriculum alone is not a strong predictor of graduation success. Academic performance during medical school and providing targeted support for students from diverse educational backgrounds are more robust predictors. Advanced predictive modelling holds promise for educational research and institutional policy development.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

The author(s) received no specific funding for this work.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study was approved by the relevant MBRU IRB Committee (Reference # MBRU IRB-2024-205) and by the Dubai Scientific Research Ethics Committee (DSREC), Dubai Health Authority (DSREC-GL10-2024).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

Data are available from the Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) Institutional Data Access / Ethics Committee (contact via IRB) for researchers who meet the criteria for access to confidential data.

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