ML models performance (RFE).

Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.抑郁是一种常对个体产生负面影响的心理状态。尽管各年龄段人群均可出现抑郁症状，但学生群体在整个求学阶段尤为易感。自2020年起，新型冠状病毒肺炎（COVID-19）疫情通过实施隔离管控，迫使人们长期依赖移动设备开展沟通联络，使得移动互联成为疫情期间及后疫情时代的新常态，给民众生活造成了诸多严峻挑战。随着高校逐步转向混合式教学模式，学生群体所面临的此类状况进一步加剧。在此背景下，通过移动设备与网络使用行为监测学生的心理健康状态，对其身心健康至关重要。本研究以孟加拉国某国际大学的学生为研究对象，探讨其因长期使用移动设备（如智能手机、平板电脑、笔记本电脑等）所导致的心理健康问题。本研究采用横断面调查法，共收集到444名受访者的有效数据。完成探索性数据分析后，本研究引入八种机器学习（Machine Learning, ML）算法，构建了一套可自动识别并分类正常至极重度抑郁的系统。当该自动检测系统结合卡方检验（Chi-square test）与递归特征消除（Recursive Feature Elimination, RFE）等特征选择方法时，模型准确率提升约3%至5%。同理，当采用主成分分析（Principal Component Analysis, PCA）等特征提取方法时，模型准确率提升5%至15%。此外，稀疏主成分分析（SparsePCA）特征提取技术与CatBoost分类器相结合的方案，在准确率、F1值与受试者工作特征曲线下面积（ROC-AUC）三项指标上均取得最优表现。数据分析结果显示，约44%的受访者未表现出抑郁症状；约25%的学生存在轻度至中度抑郁症状，另有31%的学生表现为重度至极重度抑郁症状。研究结果表明，结合恰当特征工程方法的机器学习模型，可有效应用于学生群体的多阶段抑郁筛查。该模型亦可推广至其他领域，用于人群早期抑郁症状的识别。