利用挖掘技术分析学生心理健康数据集

Journal of Computer Science Pub Date : 2024-01-01 DOI:10.3844/jcssp.2024.121.128

Yemima Monica Geasela, D. Y. Bernanda, Johanes Fernandes, J. Andry, Christian Kurniadi Jusuf, Samuel Winata, Shierly Everlin

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引用次数: 0

摘要

:本研究利用 RapidMiner 中的决策树模型来分析来自 Kaggle 的数据集，该数据集由 200 条学生记录组成。其中，70 名学生报告了心理健康问题，130 名学生没有报告。令人吃惊的是，在 70 名有心理健康问题的学生中，有 58 名学生没有向专业人士寻求帮助。这项研究强调了学生对心理健康服务利用不足这一紧迫问题，并提出了切实可行的解决方案，如加强宣传和教育、改善心理健康服务的获取途径、提供同伴支持以及解决潜在问题。研究设计包括符合道德标准的数据收集方法和决策树模型的分析应用。本研究的贡献在于，它发现了有心理健康问题的学生不寻求帮助的普遍现象，并提出了解决这一关键问题的方案。

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Analysis of Student Mental Health Dataset Using Mining Techniques

: This study utilizes a decision tree model in RapidMiner to analyze a dataset from Kaggle, comprising 200 student records. Among these, 70 students reported mental health issues, while 130 did not. Strikingly, a significant majority of 58 out of the 70 students with mental health concerns do not seek assistance from professionals. This study underscores the pressing issue of underutilization of mental health services among students and offers practical solutions, such as enhancing awareness and education, improving access to mental health services, providing peer support, and addressing underlying issues. The research design includes data collection methods that maintained ethical standards and the decision tree model's application for analysis. This study's contribution lies in its identification of the prevalence of students with mental health issues who do not seek help and the proposed solutions to address this critical issue.

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来源期刊

Journal of Computer Science Computer Science-Computer Networks and Communications

CiteScore

1.70

自引率

0.00%

发文量

期刊介绍： Journal of Computer Science is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. JCS updated twelve times a year and is a peer reviewed journal covers the latest and most compelling research of the time.