ArticleBMC public health2025
Predictors of depression among Chinese college students: a machine learning approach.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Explainable Machine Learning for Predicting Student Depression Risk.Bioengineering (Basel, Switzerland) · 2026Article
- Revealing symptom-level vulnerabilities between academic involution and anxiety: a network analysis.BMC psychology · 2026Article
- Latent profile analysis of depressive symptoms and its associations with problematic social media use and demographic characteristics among Chinese college students.BMC psychology · 2026Article
- Mapping Heterogeneity in Psychological Risk Among University Students Using Explainable Machine Learning.Entropy (Basel, Switzerland) · 2026Article
- Machine learning-based identification of anxiety symptoms in Chinese community-dwelling older adults: a comparative study of six algorithms with SHAP analysis and nomogram development.Frontiers in psychiatry · 2026Article
- Depressive symptoms as a mediator in the association between regular physical activity and irritable bowel syndrome-related symptom severity: a cross-sectional study among college students.Frontiers in psychology · 2026Article
- Deep learning with ensemble-based hybrid AI model for bipolar and unipolar depression detection using demographic and behavioral based on time-series data.Dialogues in clinical neuroscience · 2025Article
- Associations of physical activity, sedentary time, sleep quality, and resting EEG with mild depressive symptoms in college students: a cross-sectional study.BMC public health · 2025Article
- Assessing Health Behaviors as Predictors of Psychological Resilience and BMI in a National and Small Military College Sample.Healthcare (Basel, Switzerland) · 2025Article
- Machine learning-based nomogram for predicting depressive symptoms in women: A cross-sectional study in Guangdong Province, China.World journal of psychiatry · 2025Article
- Association of weekend catch-up sleep ratio with depressive risk: insights from NHANES 2021-2023.BMC psychiatry · 2025Article
- Mental Health Issues and 24-Hour Movement Guidelines-Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Machine Learning Approach.Journal of medical Internet research · 2025Article
- Machine learning models for predicting the risk of depressive symptoms in Chinese college students.Frontiers in psychiatry · 2025Article
- The bidirectional relationship between depression and perceived social support among college freshmen: evidence from longitudinal and daily diary methods.Frontiers in psychology · 2025Article
- Prevalence and predictors of anxiety, depression, and suicidal ideation among Namibian public university students.The South African journal of psychiatry : SAJP : the journal of the Society of Psychiatrists of South Africa · 2025Article
- Cardiovascular disease and depression: a bidirectional relationship and its clinical implications.Frontiers in psychiatry · 2025Review
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8 authors.
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Abstract
backgroundDepression is highly prevalent among college students, posing a significant public health challenge. Identifying key predictors of depression is essential for developing effective interventions. This study aimed to analyze potential depression risk factors among Chinese college students using the Random Forest Algorithm (RFA) and to explore gender differences in risk patterns.
methodsA cross-sectional study was conducted with 10,043 undergraduate students from Guizhou Normal University. Thirty-three variables were analyzed using RFA. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D), with a score of ≥ 16 indicating depression risk. The variables included sociodemographic characteristics, physical and psychological health indicators, behavioral and lifestyle factors, socioeconomic conditions, and family mental health history.
resultsThe RFA identified several factors associated with depression risk, with suicidal ideation, anxiety, and sleep quality exhibiting the strongest associations. Other significant predictors included academic stress, BMI, vital capacity, psychological resilience, physical fitness test scores, major satisfaction, and social network use. The model achieved an accuracy of 87.5% and an AUC of 0.927. Gender-stratified analysis suggested different patterns: physical fitness indicators showed stronger associations with depression risk among male students, while BMI was more strongly associated with depression risk among female students.
conclusionsThis cross-sectional study identified factors associated with depression risk among Chinese college students, with psychological factors showing the strongest associations. Gender-specific patterns were observed, suggesting the importance of considering gender differences when developing mental health interventions. However, longitudinal studies are required to establish causal relationships and validate these findings through intervention trials.
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