Using machine learning methods to predict post-traumatic stress disorder in stroke patients in China.

ABSTRACT:

Background

This study aims to utilize various machine learning algorithms to construct a risk prediction model for post-stroke Post-Traumatic Stress Disorder (PTSD), select the optimal model, and identify risk factors.

Methods

A total of 249 stroke patients from two tertiary hospitals in Jiangsu Province and Shandong Province were selected and randomly divided into the training group and the validation group. Based on the results of Logistic regression analysis, a risk prediction model for PTSD after stroke was constructed by using Logistic regression, Random forest (RF) and K-nearest neighbor algorithm, and further verification was conducted according to the best algorithm.

Results

The incidence of PTSD in stroke patients was 40.56%, and the RF model was the best. Fe

SUBMITTER: Li Y 

PROVIDER: S-EPMC12675482 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature