Proposing Two Hybrid Data Mining Models for Discovering Students' Mental Health Problems

IF 0.8 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Acta Informatica Pragensia Pub Date : 2021-06-30 DOI:10.18267/j.aip.148
Shabnam Shadroo, Mohsen Yoosefi Nejad, Samira Tavanaiee Yosefian, M. Naserbakht, M. Hosseinzadeh
{"title":"Proposing Two Hybrid Data Mining Models for Discovering Students' Mental Health Problems","authors":"Shabnam Shadroo, Mohsen Yoosefi Nejad, Samira Tavanaiee Yosefian, M. Naserbakht, M. Hosseinzadeh","doi":"10.18267/j.aip.148","DOIUrl":null,"url":null,"abstract":"Mental health is an important issue for university students. The objective of this article was to apply and compare the different classification methods for students’ mental health problems. Furthermore, it presents an ensemble classification method to improve the accuracy of classifiers and assist psychologists in the decision making process. For this, 10 different classifiers were applied for classifying students into two groups. In addition, two methods of combining the classifiers are presented. In the first proposed method, the classifiers were selected based on their accuracy, and then voting was carried out based on maximum probability. In the second proposed method, the methods were combined based on the fields of the confusion table, and the voting was carried out based on majority voting scheme. These two methods were evaluated in two ways. Focusing on the accuracy and the maximum probability voting, the accuracy of the first method was 92.24%, whereas in the second method, it was 95.97%. Further, using confusion table and majority voting applied to the entire dataset, the accuracy reached 96.66%. The results are promising to assist the process of mental health assessment of students.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Informatica Pragensia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18267/j.aip.148","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Mental health is an important issue for university students. The objective of this article was to apply and compare the different classification methods for students’ mental health problems. Furthermore, it presents an ensemble classification method to improve the accuracy of classifiers and assist psychologists in the decision making process. For this, 10 different classifiers were applied for classifying students into two groups. In addition, two methods of combining the classifiers are presented. In the first proposed method, the classifiers were selected based on their accuracy, and then voting was carried out based on maximum probability. In the second proposed method, the methods were combined based on the fields of the confusion table, and the voting was carried out based on majority voting scheme. These two methods were evaluated in two ways. Focusing on the accuracy and the maximum probability voting, the accuracy of the first method was 92.24%, whereas in the second method, it was 95.97%. Further, using confusion table and majority voting applied to the entire dataset, the accuracy reached 96.66%. The results are promising to assist the process of mental health assessment of students.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
提出两种发现学生心理健康问题的混合数据挖掘模型
心理健康是大学生面临的一个重要问题。本文的目的是应用和比较不同的分类方法对学生的心理健康问题。在此基础上,提出了一种集成分类方法,以提高分类器的准确率,帮助心理学家进行决策。为此,我们使用了10种不同的分类器将学生分为两组。此外,还提出了两种组合分类器的方法。在第一种方法中,根据分类器的准确率选择分类器,然后根据最大概率进行投票。在第二种方法中,基于混淆表的字段组合方法,并基于多数投票方案进行投票。对这两种方法进行了两种评价。以准确性和最大概率投票为重点,第一种方法的准确率为92.24%,第二种方法的准确率为95.97%。进一步,将混淆表和多数投票应用于整个数据集,准确率达到96.66%。研究结果对学生心理健康评估具有一定的指导意义。
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 去求助
来源期刊
Acta Informatica Pragensia
Acta Informatica Pragensia Social Sciences-Library and Information Sciences
CiteScore
1.70
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
Evaluation of the I-Voting System for Remote Primary Elections of the Czech Pirate Party Investigating the Causes of Non-realization of Project Prediction and Proposal of a New Prediction Framework The Fairness Stitch: A Novel Approach for Neural Network Debiasing Blockchain-Powered Patient-Centric Access Control with MIDC AES-256 Encryption for Enhanced Healthcare Data Security Information Ethics in Light of Bibliometric Analyses: Discovering a Shift to Ethics of Artificial Intelligence
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1