文章基本信息

标题：Proposing Two Hybrid Data Mining Models for Discovering Students' Mental Health Problems
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作者：Shabnam Shadroo ; Mohsen Yoosefi Nejad ; Samira Tavanaiee Yosefian 等
期刊名称：Acta Informatica Pragensia
电子版ISSN：1805-4951
出版年度：2021
卷号：10
期号：1
页码：85-107
DOI：10.18267/j.aip.148
语种：Czech
出版社：University of Economics, Prague
摘要：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.