基于机器学习的算法预测孟加拉国大学生抑郁和焦虑的性能比较:第一波新冠肺炎大流行的结果

IF 5 Q1 PSYCHIATRY
M. Nayan, M. Uddin, M. Hossain, M. Alam, M. Zinnia, Iqramul Haq, Md Rahman, Rejwana Ria, Md Haq Methun
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引用次数: 16

摘要

本研究的目的是使用各种机器学习(ML)算法来预测大学生的精神疾病。方法:对居住在孟加拉国的2121名大学生(私立和公立)进行结构化的在线问卷调查。在获得知情同意后,参与者完成了一项基于网络的调查,检查社会人口统计学变量和行为测试(包括患者健康问卷(PHQ-9)量表和广泛性焦虑障碍评估-7量表)。本研究采用logistic回归、随机森林(RF)、支持向量机(SVM)、线性判别分析、k近邻、Naïve贝叶斯等六种著名的机器学习算法,对孟加拉国达卡市大学生的精神疾病进行预测。结果:在符合条件的2121名受访者中,男性占45%,女性占55%,年龄在21-25岁之间的约占76.9%。女性患严重抑郁和严重焦虑的比例高于男性。综合各性能参数,准确度评估结果显示,RF预测抑郁的准确度优于其他模型(89%),而SVM预测焦虑的准确度优于其他模型(91.49%)。结论:基于这些发现,我们建议RF算法和SVM算法比本研究中使用的任何其他ML算法在预测孟加拉国大学生心理健康状况(分别为抑郁和焦虑)方面更为温和。最后,本研究提出将射频和支持向量机分类应用于精神疾病状态的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of the performance of machine learning-based algorithms for predicting depression and anxiety among University Students in Bangladesh: A result of the first wave of the COVID-19 pandemic
Introduction: The purpose of this research was to predict mental illness among university students using various machine learning (ML) algorithms. Methods: A structured questionnaire-based online survey was conducted on 2121 university students (private and public) living in Bangladesh. After obtaining informed consent, the participants completed a web-based survey examining sociodemographic variables and behavioral tests (including the Patient Health Questionnaire (PHQ-9) scale and the Generalized Anxiety Disorder Assessment-7 scale). This study applied six well-known ML algorithms, namely logistic regression, random forest (RF), support vector machine (SVM), linear discriminate analysis, K-nearest neighbors, Naïve Bayes, and which were used to predict mental illness among university students from Dhaka city in Bangladesh. Results: Of the 2121 eligible respondents, 45% were male and 55% were female, and approximately 76.9% were 21–25 years old. The prevalence of severe depression and severe anxiety was higher for women than for men. Based on various performance parameters, the results of the accuracy assessment showed that RF outperformed other models for the prediction of depression (89% accuracy), while SVM provided the best result than other models for the prediction of anxiety (91.49% accuracy). Conclusion: Based on these findings, we recommend that the RF algorithm and the SVM algorithm were more moderate than any other ML algorithm used in this study to predict the mental health status of university students in Bangladesh (depression and anxiety, respectively). Finally, this study proposes to apply RF and SVM classification when the prediction of mental illness status is the core interest.
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来源期刊
Asian Journal of Social Health and Behavior
Asian Journal of Social Health and Behavior Social Sciences-Health (social science)
CiteScore
8.50
自引率
0.00%
发文量
18
审稿时长
17 weeks
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