Mumenin Nasirul, Kabir Hossain A B M, Hossain Md Arafat, Debnath Partha Pratim, Nusrat Della Mursheda, Hasan Rashed Md Mahmudul, Hossen Afzal, Basar Md Rubel, Hossain Md Sejan
Bangladesh Army University of Engineering and Technology, Rajshahi, Bangladesh.
Heliyon. 2024 Sep 2;10(17):e37182. doi: 10.1016/j.heliyon.2024.e37182. eCollection 2024 Sep 15.
The escalating incidence of depression has brought attention to the increasing concern regarding the mental well-being of university students in the current academic environment. Given the increasing mental health challenges faced by students, there is a critical need for efficient, scalable, and accurate screening methods. This study aims to address the issue by using the General Health Questionnaire-12 (GHQ-12), a well recognized tool for evaluating psychological discomfort, in combination with machine learning (ML) techniques. Firstly, for effective screening of depression, a comprehensive questionnaire has been created with the help of an expert psychiatrist. The questionnaire includes the GHQ-12, socio-demographic, and job and career-related inquiries. A total of 804 responses has been collected from various public and private universities across Bangladesh. The data has been then analyzed and preprocessed. It has been found that around 60% of the study population are suffering from depression. Lastly, 16 different ML models, including both traditional algorithms and ensemble methods has been applied to examine the data to identify trends and predictors of depression in this demographic. The models' performance has been rigorously evaluated in order to ascertain their effectiveness in precisely identifying individuals who are at risk. Among the ML models, Extremely Randomized Tree (ET) has achieved the highest accuracy of 90.26%, showcasing its classification effectiveness. A thorough investigation of the performance of the models compared, therefore clarifying their possible relevance in the early detection of depression among university students, has been presented in this paper. The findings shed light on the complex interplay among socio-demographic variables, stressors associated with one's profession, and mental well-being, which offer an original viewpoint on utilizing ML in psychological research.
抑郁症发病率的不断上升，使人们更加关注当前学术环境下大学生的心理健康状况。鉴于学生面临的心理健康挑战日益增多，迫切需要高效、可扩展且准确的筛查方法。本研究旨在通过使用一般健康问卷-12（GHQ-12）（一种公认的评估心理不适的工具）与机器学习（ML）技术相结合来解决这一问题。首先，为了有效筛查抑郁症，在一位专家精神科医生的帮助下创建了一份综合问卷。该问卷包括GHQ-12、社会人口统计学以及与工作和职业相关的询问。从孟加拉国各地的公立和私立大学共收集了804份回复。然后对数据进行了分析和预处理。结果发现，约60%的研究人群患有抑郁症。最后，应用了16种不同的ML模型，包括传统算法和集成方法，来检查数据，以识别该人群中抑郁症的趋势和预测因素。对模型的性能进行了严格评估，以确定它们在准确识别有风险个体方面的有效性。在ML模型中，极端随机树（ET）达到了最高准确率90.26%，展示了其分类有效性。本文对所比较模型的性能进行了全面研究，从而阐明了它们在大学生抑郁症早期检测中的可能相关性。研究结果揭示了社会人口统计学变量、与职业相关的压力源和心理健康之间的复杂相互作用，为在心理学研究中利用ML提供了一个全新的视角。
