ORIGINAL RESEARCH article
Front. Surg.
Sec. Orthopedic Surgery
Volume 12 - 2025 | doi: 10.3389/fsurg.2025.1591671
This article is part of the Research TopicAdvances in Precision Medicine for Minimally Invasive Treatment of Pelvis/Hip Fractures: Integration of Digital and Intelligent TechnologiesView all 6 articles
A Machine Learning Model for Predicting Postoperative Complication Risk in Young and Middle-Aged Patients with Femoral Neck Fractures
Provisionally accepted
- Fuzhou Second General Hospital, Fuzhou, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective: Femoral neck fractures are the most common type of hip fracture, and the postoperative complications associated with these fractures significantly affect patients' quality of life and healthcare costs. The objective of this study was to develop a predictive model using machine learning (ML) techniques to assess the risk of postoperative complications in young and middle-aged patients with femoral neck fractures.Methods：We retrospectively analyzed data from 899 young and middle-aged patients with femoral neck fractures who underwent surgical treatment between September 2019 and June 2024.Key predictors affecting postoperative complications were identified through LASSO regression and multifactorial logistic regression analyses. Several machine learning (ML) models were then integrated for comparative analysis. Ultimately, the best-performing model was selected, and its interpretation was provided using SHAP values to offer a personalized risk assessment.： The study results indicate that intraoperative reduction quality, medial cortex comminution, fracture types, posterior tilt angle, early postoperative weight-bearing, and removal of internal fixation devices are significant predictors of postoperative complications. The logistic regression model demonstrated the best performance on the test set, with an area under the curve (AUC) of 0.906, accuracy of 0.877, sensitivity of 0.748, and specificity of 0.903. Additionally, SHAP analysis identified the seven most important features in the model, providing clinicians with an intuitive tool for risk assessment. Conclusions: This study successfully developed and validated a logistic regression-based predictive model, augmented with SHAP explanations, providing an effective tool for assessing the risk of postoperative complications in young and middle-aged patients with femoral neck fractures.
Keywords: Femoral neck fracture, Internal fixation failure, Risk factors, machine learning, Prediction model
Received: 12 Mar 2025; Accepted: 18 Jul 2025.
Copyright: © 2025 Huang, Lin, Chen, Jiang and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Fengfei Lin, Fuzhou Second General Hospital, Fuzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.