Prediction and Feature Selection of Mastectomy-Related Post Traumatic Stress Disorder (PTSD) Using Machine Learning...

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.

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.

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.

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.