[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126327-en":3,"doc-seo-126327-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126327,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","A machine learning model for predicting postoperative complication risk in young and middle-aged patients with femoral neck fractures","Femoral neck fractures account for a substantial share of hip fractures, and postoperative complications significantly worsen quality of life while increasing healthcare costs. This study develops a machine-learning predictive model to estimate complication risk in young and middle-aged patients undergoing surgery. Data from 899 patients were analyzed using LASSO and multifactor logistic regression to identify key predictors. Multiple ML models were compared, and the top-performing logistic regression model was interpreted with SHAP for personalized risk assessment.","TYPE Original Research PUBLISHED 26 August 2025  \nDOI 10.3389/fsurg.2025.1591671  \nEDITED BY  \nYu Wang,  \nBeihang University, China  \nREVIEWED BY  \nSun Wei,  \nShenzhen Second People’s Hospital, China Xing Qiu,  \nDalian University, China  \n*CORRESPONDENCE  \nFengfei Lin  \n [596558644@qq.com](596558644@qq.com)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 12 March 2025  \nACCEPTED 18 July 2025  \nPUBLISHED 26 August 2025  \nCITATION  \nHuang Y, Lin D, Chen B, Jiang X, Shangguan Sand Lin F (2025) A machine learning model for predicting postoperative complication risk in young and middle-aged patients with femoral neck fractures.  \nFront. Surg. 12:1591671 .  \ndoi: 10.3389/fsurg.2025.1591671  \nCOPYRIGHT  \n© 2025 Huang, Lin, Chen, Jiang, Shangguan 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) and the copyright owner(s) 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.  \nA machine learning model for predicting postoperative complication risk in young and middle-aged patients with femoral neck fractures  \nYixin Huang1,2† , Dongze Lin1† , Bin Chen1†, Xiaole Jiang1 , Shanglin Shangguan3 and Fengfei Lin1*   \n1Department of Orthopedics, Fuzhou Second General Hospital, Fujian Provincial Clinical Medical Research Center for Trauma Orthopedics Emergency and Rehabilitation, Fuzhou, China, 2Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China, 3Anxi County Hospital, Quanzhou, Fujian, China  \nObjective: 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.  \nMethods: 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 bestperforming model was selected, and its interpretation was provided using SHAP values to offer a personalized risk assessment.  \nResults: 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.  \nKEYWORDS  \nfemoral neck fracture, internal fixation failure, risk factors, machine learning, prediction model  \nFrontiers in Surgery 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nHip fractures are reported to occur more than 1.7 million times globally each year (1), with femoral neck fractures accounting fo","cbCaicGOALTAKKaC","https://ap.wps.com/l/cbCaicGOALTAKKaC","pdf",2931495,11,1,16,"English","en",105,"# Objective\n# Methods\n## Predictors and model development\n## Model comparison and interpretation\n# Results\n## Key predictors\n## Predictive performance\n# Conclusions\n## Logistic regression with SHAP explanations","[{\"question\":\"What was the primary objective of the study?\",\"answer\":\"To develop a machine learning model that predicts the risk of postoperative complications in young and middle-aged patients with femoral neck fractures.\"},{\"question\":\"How were key predictors selected for the model?\",\"answer\":\"Key predictors were identified using LASSO regression and multifactorial logistic regression analyses.\"},{\"question\":\"What model performed best and how was it evaluated?\",\"answer\":\"The logistic regression model showed the best performance on the test set, with AUC 0.906, accuracy 0.877, sensitivity 0.748, and specificity 0.903.\"}]","A machine learning model for predicting postoperative complication risk in young and middle-aged patients with femoral neck fractures | 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was the primary objective of the study?","Question",{"text":77,"@type":78},"To develop a machine learning model that predicts the risk of postoperative complications in young and middle-aged patients with femoral neck fractures.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How were key predictors selected for the model?",{"text":82,"@type":78},"Key predictors were identified using LASSO regression and multifactorial logistic regression analyses.",{"name":84,"@type":75,"acceptedAnswer":85},"What model performed best and how was it evaluated?",{"text":86,"@type":78},"The logistic regression model showed the best performance on the test set, with AUC 0.906, accuracy 0.877, sensitivity 0.748, and specificity 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