Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: A preliminary study.

Purpose/objectives: Admission into dental school involves selecting applicants for successful completion of the course. This study aimed to predict the academic performance of Kulliyyah of Dentistry, International Islamic University Malaysia pre-clinical dental students based on admission results using artificial intelligence machine learning (ML) models, and Pearson correlation coefficient (PCC).

Methods: ML algorithms logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) models were applied.