[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124968-en":3,"doc-seo-124968-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124968,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Predicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders in active population - a machine learning evaluation","This study evaluates and predicts the long-term effectiveness of five lifestyle interventions for individuals with eating disorders using machine learning. Participants were initially diagnosed with the Eating Disorder Diagnostic Scale (EDDS) and outcomes were tracked from baseline through and at the end of the intervention period. Body fat percentage, waist-hip ratio, fasting blood sugar, LDL and total cholesterol, weight, and triglycerides were measured. Random Forest and Gradient Boosting models achieved high predictive accuracy, then ranked interventions by predicted long-term effectiveness. Findings support tailored health strategies and predictive healthcare using ML.","OPEN ACCESS  \nEDITED BY  \nAlessandra Pokrajac-Bulian, University of Rijeka, Croatia  \nREVIEWED BY  \nAlexandre Puttick,  \nBern University of Applied Sciences, Switzerland  \nPetra Anić,  \nUniversity of Rijeka, Croatia Yu Wang,  \nSichuan University, China  \n*CORRESPONDENCE  \nKhadijeh Irandoust  \n [irandoust@ikiu.ac.ir](irandoust@ikiu.ac.ir)[ ](irandoust@ikiu.ac.ir)Beat Knechtle  \n beat. knechtle@hispeed.ch RECEIVED 23 February 2024 ACCEPTED 15 July 2024 PUBLISHED 07 August 2024  \nCITATION  \nIrandoust K, Parsakia K, Estifa A, Zoormand G, Knechtle B, Rosemann T, Weiss K and Taheri M (2024) Predicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders in active population: a machine learning evaluation.  \nFront. Nutr. 11:1390751 .  \ndoi: 10.3389/fnut.2024.1390751  \nCOPYRIGHT  \n© 2024 Irandoust, Parsakia, Estifa, Zoormand, Knechtle, Rosemann, Weiss and Taheri. This isan 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.  \nTYPE Original Research PUBLISHED 07 August 2024 DOI 10.3389/fnut.2024.1390751  \nPredicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders in active population: a machine learning evaluation  \nKhadijeh Irandoust 1*, Kamdin Parsakia 2, Ali Estifa 1, Gholamreza Zoormand3, Beat Knechtle4*, Thomas Rosemann 5, Katja Weiss 5 and Morteza Taheri 6  \n1 Department of Sport Sciences, Imam Khomeini International University, Qazvin, Iran, 2 Department of Psychology and Counseling, KMAN Research Institute, Richmond Hill, ON, Canada, 3 Department of Physical Education, Huanggang Normal University, Huanggang, China, 4 Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland, 5 Institute of Primary Care, University of Zürich, Zürich, Switzerland, 6 Department of Cognitive and Behavioural Sciences in Sport, Faculty of Sport Science and Health, University of Tehran, Tehran, Iran  \nObjective: This study aims to evaluate and predict the long-term effectiveness of five lifestyle interventions for individuals with eating disorders using machine learning techniques.  \nMethods: This study, conducted at Dr. Irandoust’s Health Center at Qazvin from August 2021 to August 2023, aimed to evaluate the effects of five lifestyle interventions on individuals with eating disorders, initially diagnosed using The Eating Disorder Diagnostic Scale (EDDS) . The interventions were: (1) Counseling, exercise, and dietary regime, (2) Aerobic exercises with dietary regime, (3) Walking and dietary regime,(4) Exercise with a flexible diet, and (5) Exercises through online programs and applications. Out of 955 enrolled participants, 706 completed the study, which measured Body Fat Percentage (BFP), WaistHip Ratio (WHR), Fasting Blood Sugar (FBS), Low-Density Lipoprotein (LDL) Cholesterol, Total Cholesterol (CHO), Weight, and Triglycerides (TG) at baseline, during, and at the end of the intervention. Random Forest and Gradient Boosting Regressors, following feature engineering, were used to analyze the data, focusing on the interventions’ long-term effectiveness on health outcomes related to eating disorders.  \nResults: Feature engineering with Random Forest and Gradient Boosting Regressors, respectively, reached an accuracy of 85 and 89%, then 89 and 90% after dataset balancing. The interventions were ranked based on predicted effectiveness: counseling with exercise and dietary regime, aerobic exercises with dietary regime, walking with dietary regime, exercise with a flexible diet, and exercises through online programs.  \nConclusion: The results show that Machine Learning (ML) model","cbCaitOM4kopyqLL","https://ap.wps.com/l/cbCaitOM4kopyqLL","pdf",1137220,1,13,"English","en",105,"# Objective\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To evaluate and predict the long-term effectiveness of five lifestyle interventions for individuals with eating disorders using machine learning techniques.\"},{\"question\":\"Which machine learning models were used?\",\"answer\":\"The study used Random Forest and Gradient Boosting Regressors, after feature engineering and dataset balancing.\"},{\"question\":\"How were the interventions compared in the results?\",\"answer\":\"Interventions were ranked according to predicted effectiveness, based on model outputs derived from measured health indicators.\"}]","Predicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders in active population - 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