A risk prediction model based on machine learning algorithm for parastomal hernia after permanent colostomy.

ABSTRACT:

Objective

To develop a machine learning-based risk prediction model for postoperative parastomal hernia (PSH) in colorectal cancer patients undergoing permanent colostomy, assisting nurses in identifying high-risk groups and devising preventive care strategies.

Methods

A case-control study was conducted on 495 colorectal cancer patients who underwent permanent colostomy at the Second Affiliated Hospital of Anhui Medical University from June 2017 to June 2023, with a 1-year follow-up period. Patients were categorized into PSH and non-PSH groups based on PSH occurrence within 1-year post-operation. Data were split into training (70%) and testing (30%) sets. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and binary cla

SUBMITTER: Dai T 

PROVIDER: S-EPMC11308496 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature