[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126361-en":3,"doc-seo-126361-105":31,"detail-sidebar-cat-0-en-105":92},{"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},126361,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Application of machine learning in predicting perioperative neurocognitive disorders in elderly patients - the impact of sarcopenia-related features","Machine learning models were developed to predict perioperative neurocognitive disorders (PND) in elderly patients undergoing non-cardiac surgery, motivated by the clinical need for earlier risk stratification. Sarcopenia-related features were incorporated as key predictors, and five algorithms—SVM, XGBoost, GBM, AdaBoost, and Random Forest—were trained and validated using clinical data from a single hospital cohort. SHAP interpretation was applied to identify the most influential variables. AdaBoost achieved the highest performance (AUC 0.95), suggesting strong predictive power and interpretable sarcopenia-associated risk signals for preoperative decision-making.","TYPE Original Research PUBLISHED 18 August 2025  \nDOI 10. 3389/fmed.2025.1604333  \nOPEN ACCESS  \nEDITED BY  \nAlessandro Gialluisi,  \nLUM University “Giuseppe Degennaro”, Italy  \nREVIEWED BY  \nVahid Rashedi,  \nUniversity of Social Welfare and Rehabilitation Sciences, Iran  \nAntonietta Pepe,  \nLUM University “Giuseppe Degennaro”, Italy  \n*CORRESPONDENCE  \nTianyao Zhang  \n [tianyaozhang123@163.com](tianyaozhang123@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 01 April 2025  \nACCEPTED 29 July 2025  \nPUBLISHED 18 August 2025  \nCORRECTED 03 September 2025  \nCITATION  \nQian Z, Wu X, He K, Lin K, Luo X and Zhang T (2025) Application of machine learning in predicting perioperative neurocognitive disorders in elderly patients: the impact of sarcopenia-related features.  \nFront. Med. 12:1604333 .  \ndoi: 10. 3389/fmed.2025.1604333  \nCOPYRIGHT  \n© 2025 Qian, Wu, He, Lin, Luo and Zhang. 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.  \nApplication of machine learning in predicting perioperative neurocognitive disorders in elderly patients: the impact of sarcopenia-related features  \nZhengyu Qian1,2†, Xiaochu Wu1,2,3†, Kunyang He1 , Kaijie Lin1 , Xiaobei Luo4 and Tianyao Zhang1,2*  \n1 School of Clinical Medicine, Chengdu Medical College, Chengdu, China, 2The First Affiliated Hospital of Chengdu Medical College, Chengdu, China, 3 National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China, 4 Suzhou Medical College, Soochow University, Suzhou, Jiangsu, China  \nBackground: Older surgical patients present with diverse clinical profiles, yet research indicates a significant correlation between sarcopenia-related features and the incidence of perioperative neurocognitive disorder (PND) . The integration of machine learning techniques offers a promising avenue for identifying older surgical patients at elevated risk of PND, particularly those exhibiting sarcopenia-associated characteristics. This approach enhances preoperative risk stratification and patient selection, thereby improving the precision of clinical management and treatment decisions.  \nMethods: Data were collected from patients undergoing non-cardiac surgery atthe First Affiliated Hospital of Chengdu Medical College to develop and validate a predictive model. Five machine learning models—Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Random Forest—were constructed to evaluate the risk of PND in older surgical patients. Sarcopenia-related features were incorporated as key variables in these models. The SHapley Additive exPlanations (SHAP) method was subsequently utilized to interpret the most effective model.  \nResults: A total of 443 patients were included in the study. Among the five models, AdaBoost performed best, achieving an AUC of 0.95. The six most important features identified by SHAP were 6-meter walking speed, preoperative MMSE score, maximum grip strength, appendicular skeletal muscle mass, and sarcopenia assessment age. These results demonstrate AdaBoost’s excellent predictive performance, with high interpretability and reliability.  \nConclusion: Machine learning models, particularly AdaBoost integrated with SHAP, show significant potential in predicting PND in older surgical patients. The model’s ability to clarify the impact of sarcopenia-related features enhances its clinical utility in preoperative risk assessment.  \nKEYWORDS  \nmachine learning, sarcopenia, postoperative cognitive dysfunction, SHapley Additive ex","cbCaihz38WvPmu7x","https://ap.wps.com/l/cbCaihz38WvPmu7x","pdf",959191,4,1,9,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What is the study’s goal regarding perioperative neurocognitive disorders in elderly patients?\",\"answer\":\"To build and validate machine learning predictive models for PND risk in older surgical patients, emphasizing the contribution of sarcopenia-related features to preoperative risk assessment.\"},{\"question\":\"Which machine learning models were compared, and which performed best?\",\"answer\":\"Five models were constructed: SVM, XGBoost, GBM, AdaBoost, and Random Forest. 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