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

Journal: Journal of dental education
PMID: 39080875

Abstract

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).

Authors

  • Widya Lestari
    Department of Fundamental Dental and Medical Sciences, Kulliyyah of Dentistry, International Islamic University Malaysia, Kuantan, Malaysia.
  • Adilah S Abdullah
    Kulliyyah of Dentistry, International Islamic University Malaysia, Kuantan, Malaysia.
  • Afifah M A Amin
    Kulliyyah of Dentistry, International Islamic University Malaysia, Kuantan, Malaysia.
  • Nurfaridah
    Department of Informatics, Faculty of Information Technology, Universitas YARSI, Jakarta, Indonesia.
  • Cortino Sukotjo
    Department of Prosthodontics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Azlini Ismail
    Department of Fundamental Dental and Medical Sciences, Kulliyyah of Dentistry, International Islamic University Malaysia, Kuantan, Malaysia.
  • Mohamad Shafiq Mohd Ibrahim
    Department of Paediatric Dentistry and Dental Public Health, Kulliyyah of Dentistry, International Islamic University Malaysia, Kuantan, Malaysia.
  • Nashuha Insani
    Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia.
  • Chandra P Utomo
    Department of Informatics, Faculty of Information Technology, Universitas YARSI, Jakarta, Indonesia.

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