Unsupervised Clustering of Depression Profiles in University Students Using Behavioral and Sociodemographic Features
Bibliographic record
Abstract
Depressive symptoms in different populations emerge from factors that relate to the lifestyle and background of individuals. For the university student populations, it is important to understand how sociodemographic and lifestyle characteristics influence their mental health and contribute to the development of depressive symptoms in order to come up with effective support and targeted interventions. In this study, we apply unsupervised Machine Learning (ML) to identify depression-related patterns in university students using a set of behavioral and demographic features collected from wearable devices and questionnaires. Using a selected set of features that includes parental education, income, academic activity, and daily behavioral and physiological measures, we clustered student profiles and assessed how these structural factors relate to depressive symptoms measured through the Patient Health Questionnaire-9 (PHQ-9) outcome. Feature importance and ANOVA analyses were used to evaluate cluster formation and interpretability. Our findings suggest that behavioral features, such as walking activity levels and exercise patterns, have a major role in producing depression-related clusters. Moreover, we observed meaningful variability and associations between demographic factors, like parental education and study time, with the depression outcomes. This work contributes to the growing field of fair and explainable ML in mental health and emphasizes the potential of data-driven tools for personalized decision-making for mental health screening in students and younger populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".