Chowdhury Arman Hossain, Rad Dana, Rahman Md Siddikur
Department of Statistics Begum Rokeya University Rangpur Bangladesh.
Center of Research Development and Innovation in Psychology Aurel Vlaicu University of Arad Arad Romania.
Health Sci Rep. 2024 Apr 21;7(4):e2037. doi: 10.1002/hsr2.2037. eCollection 2024 Apr.
Mental health problem is a rising public health concern. People of all ages, specially Bangladeshi university students, are more affected by this burden. Thus, the objective of the study was to use tree-based machine learning (ML) models to identify major risk factors and predict anxiety, depression, and insomnia in university students.
A social media-based cross-sectional survey was employed for data collection. We used Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index (ISI-7) scale for measuring students' anxiety, depression and insomnia problems. The tree-based supervised decision tree (DT), random forest (RF) and robust eXtreme Gradient Boosting (XGBoost) ML algorithms were used to build the prediction models and their predictive performance was evaluated using confusion matrix and receiver operating characteristic (ROC) curves.
Of the 1250 students surveyed, 64.7% were male and 35.3% were female. The students' ages ranged from 18 to 26 years old, with an average age of 22.24 years (SD = 1.30). Majority of the students (72.6%) were from rural areas and social media addicted (56.6%). Almost 83.3% of the students had moderate to severe anxiety, 84.7% had moderate to severe depression and 76.5% had moderate to severe insomnia problems. Students' social media addiction, age, academic performance, smoking status, monthly family income and morningness-eveningness are the main risk factors of anxiety, depression and insomnia. The highest predictive performance was observed from the XGBoost model for anxiety, depression and insomnia.
The study findings offer valuable insights for stakeholders, families and policymakers enabling a more profound comprehension of the pressing mental health disorders. This understanding can guide the formulation of improved policy strategies, initiatives for mental health promotion, and the development of effective counseling services within university campus. Additionally, our proposed model might play a critical role in diagnosing and predicting mental health problems among Bangladeshi university students and similar settings.
心理健康问题日益引起公众对健康的关注。各年龄段的人，特别是孟加拉国的大学生，受此负担的影响更大。因此，本研究的目的是使用基于树的机器学习（ML）模型来识别主要风险因素，并预测大学生的焦虑、抑郁和失眠情况。
采用基于社交媒体的横断面调查进行数据收集。我们使用广泛性焦虑障碍量表（GAD - 7）、患者健康问卷（PHQ - 9）和失眠严重程度指数（ISI - 7）量表来测量学生的焦虑、抑郁和失眠问题。使用基于树的监督决策树（DT）、随机森林（RF）和稳健的极端梯度提升（XGBoost）ML算法构建预测模型，并使用混淆矩阵和受试者工作特征（ROC）曲线评估其预测性能。
在接受调查的1250名学生中，64.7%为男性，35.3%为女性。学生年龄在18至26岁之间，平均年龄为22.24岁（标准差 = 1.30）。大多数学生（72.6%）来自农村地区且沉迷于社交媒体（56.6%）。近83.3%的学生有中度至重度焦虑，84.7%有中度至重度抑郁，76.5%有中度至重度失眠问题。学生的社交媒体成瘾、年龄、学业成绩、吸烟状况、家庭月收入和晨型 - 夜型是焦虑、抑郁和失眠的主要风险因素。XGBoost模型在焦虑、抑郁和失眠方面的预测性能最高。
研究结果为利益相关者、家庭和政策制定者提供了有价值的见解，有助于更深刻地理解紧迫的心理健康障碍。这种理解可以指导制定更好的政策策略、促进心理健康的举措以及在大学校园内发展有效的咨询服务。此外，我们提出的模型可能在诊断和预测孟加拉国大学生及类似环境中的心理健康问题方面发挥关键作用。
