Predicting mental health treatment outcomes using machine learning: Insights from a global survey dataset
Bibliographic record
Abstract
The issue of mental health is still the major public health problem around the world, and it is strong evidence of the necessity of data-driven approaches for early detection and treatment. The current research applies machine-learning techniques to ascertain the treatment-seeking behavior of the patients through a dataset of survey responses that can be accessed publicly and carried out in different countries like the U.S., Canada, and the U.K. The descriptive analyses demonstrated significant differences in the levels of stress reported, indoor confinement issues, and gender differences in treatment-seeking behavior. People experiencing higher stress and longer indoor durations were more likely to seek treatment, whereas females showed higher treatment rates than males. Logistic Regression and Random Forest are two classification models that were built and assessed in order to foretell the treatment results. The Random Forest model was the most accurate one with an accuracy of 0.73, precision ranging from 0.72 to 0.74, and recall from 0.70 to 0.76, better than Logistic Regression (accuracy = 0.70). Feature importance analysis revealed growing stress, days spent indoors, and family history as the most influential factors in the decision to seek mental health treatment. The results indicate that machine learning can thoroughly identify the risk patterns related to behavioral and demographic factors for mental health conditions. The research adds to the group of mental health studies with computer-based methods and shows the possible role of predictive analytics in promoting proactive well-being strategies and helping with focused interventions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".