Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model.

ABSTRACT:

Purpose

This study aims to develop and validate an interpretable machine learning model for predicting avascular necrosis (AVN) following talar fracture, thereby aiding in personalized prevention and treatment.

Methods

A retrospective cohort study included patients undergoing surgical intervention for talar fractures at Ningbo No.6 Hospital between January 2018 and December 2023. Multidimensional data encompassing demographic characteristics, fracture-related variables, surgery-related parameters, and follow-up information were collected. Patients were randomly allocated to the training and testing sets in a 7:3 ratio. Potential risk factors for postoperative AVN were screened using univariate and multivariate logistic regression analyses. Six machine learning algorithms wer

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12351182 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature