Abstract
OBJECTIVES: To identify the factors associated with post-stroke depression (PSD) and develop a machine learning predictive model using a large dataset, considering sociodemographic, lifestyle, and clinical factors. METHODS: Our 2025 study used data from the 2023 Behavioral Risk Factor Surveillance System, released in September 2024. Data processing was carried out using Google Colab and Python. We carried out descriptive statistics, logistic regression, and feature importance analyses (mutual information and adjusted mutual information). A total of 4 machine-learning models were trained and evaluated: random forest, decision tree, gradient boosting, and logistic regression. Model performance was assessed using the accuracy, precision, recall, harmonic mean of precision and recall (F1-score), and area under the curve - receiver operating characteristic (AUC-ROC). The best-performing model was fine-tuned using GridSearchCV with 5-fold cross-validation. RESULTS: Increasing age, male gender, being married, higher income, and physical activity were associated with lower odds of PSD. Obesity, smoking, diabetes, and high cholesterol are associated with increased odds of PSD. Age and gender were the most informative features for predicting the PSD. Random forest demonstrated the best performance for predicting PSD (accuracy=0.73, precision=0.71, recall=0.77, F1-score=0.74, and AUC-ROC=0.81), which was further improved by hyperparameter optimization. CONCLUSION: Post-stroke depression's complex etiology involves sociodemographic, lifestyle, and clinical factors, notably age and gender. A random forest model effectively predicts PSD, highlighting the need for comprehensive assessment, early intervention, and management of modifiable risks (obesity, smoking, and inactivity) to improve stroke survivors' outcomes.