Prediction and Feature Selection of Mastectomy-Related Post Traumatic Stress Disorder (PTSD) Using Machine Learning Among Breast Cancer Patients in Bangladesh.

Background: Post-mastectomy PTSD is a serious mental health issue, but it has not been studied enough, particularly in low-resource settings like Bangladesh. This study aimed to predict PTSD among breast cancer survivors using machine learning (ML) models and identify significant predictors through the Boruta algorithm, a feature selection tool, offering scalable solutions for early detection and intervention. Methods: A cross-sectional study of 138 post-mastectomy breast cancer patients was conducted across 3 hospitals in Bangladesh. Data on sociodemographic, health history, social experience, and treatment were collected using validated tools, including the PTSD Checklist for DSM-5 (PCL-5). The Boruta algorithm identified key predictors, and 10 ML models were evaluated for PTSD prediction using metrics such as accuracy, sensitivity, specificity, and AUC. Results: Random Forest (RF) outperformed other models (accuracy: 88.9%, AUC: 0.914). Significant predictors included education, monthly income, and changes in family behaviour. Factors like marital status, having chronic diseases, and hormone therapy were not statistically significant. PTSD prevalence was 34.1%, with urban residents and younger patients facing higher risks. Conclusion: ML models, particularly RF, demonstrated strong predictive performance and identified critical PTSD predictors. These findings highlight the potential for cost-effective PTSD screening in resource-constrained settings. Future research should focus on broader validation and longitudinal studies to refine predictive models.