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

IF 5.1 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2024-05-14 DOI:10.1111/jcal.12999

Gulnur Tyulepberdinova, Madina Mansurova, Talshyn Sarsembayeva, Sulu Issabayeva, Darazha Issabayeva

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引用次数: 0

Abstract

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.

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机器学习在学生健康评价中的生理、社会和心理条件

本研究旨在评估几种机器学习（ML）算法对大学生身体、社会和心理健康状况的预测效果。研究中使用的身体健康测量指标包括：BMI（体重指数）、%BF（体脂百分比）、BSC（血清胆固醇）、SBP（收缩压）和 DBP（舒张压）：心理健康评估采用了以下方法：PHQ-9（患者健康问卷-9）、ISI（失眠严重程度指数）、GAD-7（广泛性焦虑症量表）和 SBQ-R（自杀行为问卷-修订版）。该研究对社会健康综合指标 KEYES 进行了评估。研究采用了一种著名的方法来训练和测试四种著名的 ML 算法，即 K 近邻算法、决策树、奈夫贝叶斯和随机森林算法。RF 算法的 F 分数也是最高的。差异分别为 4.56%（奈夫贝叶斯）、2.50%（DT）和 11.20%（K-NN）。准确率、精确率、召回率和 F 分数被用来评估所研究的 ML 算法的预测能力。随机森林方法的预测准确率为 99.40%，精确率为 97.60%，召回率为 99.30%，F 分数为 98.70%，表现最佳。ML算法可作为预测包括学生在内的患者的身体、精神和社会健康状况的工具，但其应用范围较窄，不能涵盖健康的所有方面。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope