Predicting depression among men who have sex with men in Ghana using machine learning algorithms
Bibliographic record
Abstract
Men who have sex with men (MSM) in Ghana face heightened risks of depression due to pervasive stigma, social exclusion, and legal discrimination. Despite this, depression remains underdiagnosed and undertreated in this population. This study applied seven tree-based machine learning (ML) models using tree-based classifiers: Decision Tree, Random Forest, Gradient Boosting, AdaBoost, XGBoost, LightGBM, and CatBoost to identify key psychosocial predictors of depression in a sample of 225 MSM aged 18-60 years. The dataset included sociodemographic variables, perceived stress (PSS), social isolation (internal and external), behavioural risk indicators, and stigma-related measures. After handling missing values, data were pre-processed with feature standardization and one-hot encoding. The Synthetic Minority Over-Sampling Technique was applied to address class imbalance. Model performance was evaluated using 5-fold cross-validation and metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Among all models, Random Forest achieved the highest accuracy for the prediction of depression amongst MSM in Ghana. Feature importance analysis revealed that external social isolation (ExtSocialIso2), perceived stress (PSS14), and stigma due to same-sex behaviour (StigmaSSB9) were the most consistent predictors of depression. Variables related to resilience, gender non-conformity stigma, and sense of community belonging also contributed significantly. Depression among MSM in Ghana is closely linked to social isolation, stress, and identity-based stigma. Machine learning models, especially ensemble methods, can effectively identify individuals at risk. These findings underscore the need for culturally tailored mental health interventions and inclusive policies that address stigma and promote social support among MSM in Ghana.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".