The physical, social, and mental conditions of machine learning in student health evaluation

Tyulepberdinova G. Mansurova M. Sarsembayeva T. Issabayeva S. Issabayeva D.
October 2024 John Wiley and Sons Inc

Journal of Computer Assisted Learning
2024 #40 Issue 5 2020 - 2030 pp.

Background: This study aims to assess how well several machine learning (ML) algorithms predict the physical, social, and mental health condition of university students. Objectives: The physical health measurements used in the study include BMI (Body Mass Index), %BF (percentage of Body Fat), BSC (Blood Serum Cholesterol), SBP (Systolic Blood Pressure), and DBP (Diastolic Blood Pressure). Methods: The mental health evaluation relied on the following methods: PHQ-9 (Patient Health Questionnaire-9), ISI (Insomnia Severity Index), GAD-7 (Generalized Anxiety Disorder Scale), and SBQ-R (Suicidal Behaviors Questionnaire-Revised). The study assessed KEYES, the comprehensive social health indicator. The study uses a famous methodology for training and testing four well-known ML algorithms, namely the K-nearest neighbors algorithm, decision trees, Naïve Bayes, and the random forest algorithm. Results and Conclusions: The recall value of the RF algorithm is higher by 2.0%, 4.15%, and 11.25%, respectively. The F-score value of the RF algorithm is also the highest. The differences amount to 4.56% (Naïve Bayes), 2.50% (DT), and 11.20% (K-NN). Accuracy, Precision, Recall, and F-score were used to assess the researched ML algorithms prediction ability. With a 99.40% prediction accuracy, a 97.60% precision, a 99.30% recall, and an F-score value of 98.70%, the Random Forest method performed the best. ML algorithms can serve as tools for the prediction of physical, mental, and social health state of patients, including students, but they have a rather narrow scope of application and do not cover all aspects of health.

algorithm , f-score , precision , random forest , social and mental health

Text of the article Перейти на текст статьи

Department of Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Department of University Humanitarian Subjects, Egyptian University of Islamic Culture “Nur-Mubarak”, Almaty, Kazakhstan
Department of Informatics and Informatization of Education, Almaty Management University, Almaty, Kazakhstan

Department of Artificial Intelligence and Big Data
Department of University Humanitarian Subjects
Department of Informatics and Informatization of Education

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026