Application of Random Forest in Modeling the Prevalence of Depression among Murang’a University of Technology Students

Wahome Muthoni Loise, John W. Mutuguta, E. Nyarige
{"title":"Application of Random Forest in Modeling the Prevalence of Depression among Murang’a University of Technology Students","authors":"Wahome Muthoni Loise, John W. Mutuguta, E. Nyarige","doi":"10.9734/ajpas/2024/v26i2595","DOIUrl":null,"url":null,"abstract":"Around the world, depression is a prevalent mental illness and it affects the way people think, feel, talk and conduct their daily activities. The associated stigma often leads to misdiagnosis, posing risks such as disability and suicide. The study employed random forest algorithm to model the prevalence of depression among Murang’a University of technology (MUT) students. A sample of 1448 students from the different schools at the university participated in the study by completing questionnaires on sociodemographic and other factors associated with depression. The questionnaires were administered through social media platforms. Participants were selected using proportionate stratified random sampling and simple random sampling to ensure that a representative sample was chosen from each school. The data gathered was examined using descriptive and inferential statistics. Depression was measured using the Patient Health Questionnaire scale (PHQ-9). Using a cut-off point of 10, 25.97% students had depressive symptoms. This comprised of 19.61% moderate symptoms and 6.35% severe symptoms. The confusion matrix criteria were used to assess the performance of random forest in modeling depression prevalence among MUT students. Metrics for random forest included, accuracy (0.9868), sensitivity (0.95), specificity (1.00), positive predictive value (1.00), and negative predictive value (0.9824). Implementing targeted interventions founded on identified risk and protective factors and exploring the long-term outcomes of these interventions would contribute to the evolving field of mental health research within academic settings.","PeriodicalId":502163,"journal":{"name":"Asian Journal of Probability and Statistics","volume":"27 9","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Probability and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9734/ajpas/2024/v26i2595","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Around the world, depression is a prevalent mental illness and it affects the way people think, feel, talk and conduct their daily activities. The associated stigma often leads to misdiagnosis, posing risks such as disability and suicide. The study employed random forest algorithm to model the prevalence of depression among Murang’a University of technology (MUT) students. A sample of 1448 students from the different schools at the university participated in the study by completing questionnaires on sociodemographic and other factors associated with depression. The questionnaires were administered through social media platforms. Participants were selected using proportionate stratified random sampling and simple random sampling to ensure that a representative sample was chosen from each school. The data gathered was examined using descriptive and inferential statistics. Depression was measured using the Patient Health Questionnaire scale (PHQ-9). Using a cut-off point of 10, 25.97% students had depressive symptoms. This comprised of 19.61% moderate symptoms and 6.35% severe symptoms. The confusion matrix criteria were used to assess the performance of random forest in modeling depression prevalence among MUT students. Metrics for random forest included, accuracy (0.9868), sensitivity (0.95), specificity (1.00), positive predictive value (1.00), and negative predictive value (0.9824). Implementing targeted interventions founded on identified risk and protective factors and exploring the long-term outcomes of these interventions would contribute to the evolving field of mental health research within academic settings.
随机森林在穆朗阿理工大学学生抑郁症患病率建模中的应用
在世界各地,抑郁症是一种普遍存在的精神疾病,它影响着人们的思维、感觉、谈话和日常活动方式。与之相关的耻辱感往往导致误诊,带来残疾和自杀等风险。这项研究采用随机森林算法,对穆朗阿理工大学(MUT)学生的抑郁症患病率进行建模。来自该大学不同学院的 1448 名学生通过填写与抑郁症相关的社会人口和其他因素的调查问卷参与了这项研究。问卷通过社交媒体平台发放。研究人员采用比例分层随机抽样和简单随机抽样的方法选取参与者,以确保从每所学校选取的样本具有代表性。收集到的数据采用描述性和推论性统计方法进行检验。抑郁采用患者健康问卷量表(PHQ-9)进行测量。以 10 为分界点,25.97% 的学生有抑郁症状。其中中度症状占 19.61%,重度症状占 6.35%。混淆矩阵标准用于评估随机森林在模拟UT学生抑郁症患病率方面的性能。随机森林的指标包括:准确性(0.9868)、灵敏度(0.95)、特异性(1.00)、阳性预测值(1.00)和阴性预测值(0.9824)。根据已确定的风险和保护因素实施有针对性的干预措施,并探索这些干预措施的长期效果,将有助于在学术环境中不断发展的心理健康研究领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信