Using advanced machine learning algorithms to predict academic major completion: A cross-sectional study.

Journal: Computers in biology and medicine
PMID: 39531920

Abstract

BACKGROUND: Existing prediction methods for academic majors based on personality traits have notable gaps, including limited model complexity and generalizability.The current study aimed to utilize advanced Machine Learning (ML) algorithms with smoothing functions to predict academic majors completed based on personality subscales.

Authors

  • Alireza Kordbagheri
    Department of Statistics, Mathematical Sciences, Shahid Beheshti University, Tehran, Iran. Electronic address: a.kordbagheri366@gmail.com.
  • Mohammadreza Kordbagheri
    Department of Statistics, Mathematical Sciences, Shahid Beheshti University, Tehran, Iran. Electronic address: mohammadreza366@yahoo.com.
  • Natalie Tayim
    Department of Psychology, School of Social Sciences and Humanities, Doha Institute for Graduate Studies, Doha, Qatar. Electronic address: natalie.tayim@dohainstitute.edu.qa.
  • Abdulnaser Fakhrou
    Department of Psychological Sciences, College of Education, Qatar University, Qatar. Electronic address: afakhrou@qu.edu.qa.
  • Mohammadreza Davoudi
    Department of Clinical Psychology, School of Behavioral Sciences, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. Electronic address: davoudi.phd.psy@gmail.com.

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