Zeidyen/msm-depression-ml

Code for ML-based depression prediction among MSM in Ghana using sociodemographic and psychosocial features. Includes preprocessing, feature selection, model training (Random Forest, XGBoost, etc.), evaluation metrics, and feature importance analysis. # msm-depression-ml Code for ML-based depression prediction among MSM in Ghana using sociodemographic and psychosocial features. Includes preprocessing, feature selection, model training (Random Forest, XGBoost), evaluation metrics, and feature importance analysis. # Depression Prediction among MSM in Ghana Using ML This repository contains the code, data preprocessing steps, and modeling pipeline for the manuscript titled **"Predicting Depression among Men Who Have Sex with Men in Ghana Using Machine Learning Algorithms"**, submitted to *PLOS Mental Health*. ## Repository Structure - `data/`: Contains the (anonymized) dataset of MSM population in Ghana. - `notebooks/`: Jupyter or R notebooks for exploratory data analysis and preprocessing. - `models/`: Scripts for training and evaluating the machine learning classifiers. - `results/`: Evaluation metrics, plots (ROC curves, SHAP), and model outputs. - `figures/`: Publication-ready figures and SHAP summary plots. ## Methodology Summary - **Algorithms Used**: Random Forest, XGBoost, LightGBM, CatBoost, AdaBoost, Gradient Boosting, Decision Tree. - **Key Features**: Perceived stress, social isolation, stigma, demographic info. - **Techniques**: One-hot encoding, SMOTE (for class imbalance), cross-validation. - **Evaluation**: Accuracy, ROC AUC, Precision, Recall, F1-score. - **Feature Importance**: SHAP values. ## Requirements ```bash scikit-learn xgboost lightgbm catboost shap pandas numpy matplotlib seaborn