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

ABSTRACT:

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 P

SUBMITTER: Hossain SB 

PROVIDER: S-EPMC12701936 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature