[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118729-en":3,"doc-seo-118729-105":30,"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":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},118729,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Age-specific risk factors for the prediction of obesity using a machine learning approach","Obesity is a complex condition shaped by biological, physiological, psychological, and environmental influences. This study uses KNHANES survey data to build obesity prediction models from blood test and blood pressure measurements, evaluating six machine learning classifiers. It analyzes adults aged 19–79 to identify age- and gender-specific risk factors and determine which algorithm achieves the highest predictive accuracy. Results emphasize triglycerides, ALT (SGPT), glycated hemoglobin, and uric acid, with performance varying by age and gender. The 19–39 group shows the strongest metrics, while older groups decline.","TYPE Original Research PUBLISHED 17 January 2023  \nDOI 10. 3389/fpubh.2022.998782  \nOPEN ACCESS  \nEDITED BY  \nBruno Bonnechère,  \nUniversiteit Hasselt, Belgium  \nREVIEWED BY  \nAmanda Cuevas-Sierra, IMDEA Food Institute, Spain Marco Bilucaglia, Università IULM, Italy  \n*CORRESPONDENCE  \nChunyoung Oh  \n [cyoh@jnu.ac.kr](cyoh@jnu.ac.kr)  \n†These authors have contributed equally to this work  \nSPECIALTY SECTION  \nThis article was submitted to Digital Public Health, a section of the journal Frontiers in Public Health  \nRECEIVED 21 July 2022  \nACCEPTED 06 December 2022  \nPUBLISHED 17 January 2023  \nCITATION  \nJeon J, Lee S and Oh C (2023) Age-specific risk factors for the prediction of obesity using a machine learning approach.  \nFront. Public Health 10:998782 .  \ndoi: 10.3389/fpubh.2022.998782  \nCOPYRIGHT  \n© 2023 Jeon, Lee and Oh. This is an  \nopen-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.  \nAge-specific risk factors for the prediction of obesity using a machine learning approach  \nJunhwi Jeon1†, Sunmi Lee1† and Chunyoung Oh2*  \n1 Department of Applied Mathematics, Kyung Hee University, Yongin, South Korea, 2 Department of Mathematics Education, Chonnam National University, Gwangju, South Korea  \nMachine Learning is a powerful tool to discover hidden information and relationships in various data-driven research fields. Obesity is an extremely complex topic, involving biological, physiological, psychological, and environmental factors. One successful approach to the topic is machine learning frameworks, which can reveal complex and essential risk factors of obesity. Over the last two decades, the obese population (BMI of above 23) in Korea has grown. The purpose of this study is to identify risk factors that predict obesity using machine learning classifiers and identify the algorithm with the best accuracy among classifiers used for obesity prediction. This work will allow people to assess obesity risk from blood tests and blood pressure data based on the KNHANES, which used data constructed by the annual survey. Our data include a total of 21,100 participants (male 10,000 and female 11,100) . We assess obesity prediction by utilizing six machine learning algorithms. We explore age-and gender-specific risk factors of obesity for adults (19–79 years old) . Our results highlight the four most significant features in all age-gender groups for predicting obesity: triglycerides, ALT (SGPT), glycated hemoglobin, and uric acid. Our findings show that the risk factors for obesity are sensitive to age and gender under di􀀀erent machine learning algorithms. Performance is highest for the 19–39 age group of both genders, with over 70% accuracy and AUC, while the 60–79 age group shows around 65% accuracy and AUC. For the 40–59 age groups, the proposed algorithm achieved over 70% in AUC, but for the female participants, it achieved lower than 70% accuracy. For all classifiers and age groups, there is no big di􀀀erence in the accuracy ratio when the number of features is more than six; however, the accuracy ratio decreased in the female 19–39 age group.  \nKEYWORDS  \nobesity prediction, machine learning, age-specific, gender-specific, risk factors, KNHANES  \n1. Introduction  \nThe prevalence of obesity has become one of the most prominent issues in global public health. The causes of obesity fall into several categories, including physiology, individual psychology, food production, food consumption, physiology, individual physical activity, genetic and cultural in􀀃uence, and physical activity environment (1, 2). With the number of obese people doubling in two decades (from 1","cbCaigbqR9GPBBpF","https://ap.wps.com/l/cbCaigbqR9GPBBpF","pdf",12513281,1,12,"English","en",105,"# Introduction\n## Obesity prevalence and public health impact\n## Definition and measurement (BMI)\n# Study purpose and approach\n## Prediction using machine learning classifiers\n## Age- and gender-specific risk factors","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To identify risk factors that predict obesity using machine learning classifiers and to determine the algorithm with the best accuracy among the tested models.\"},{\"question\":\"What data source and measurements are used for prediction?\",\"answer\":\"The study uses KNHANES data and evaluates predictors derived from blood tests and blood pressure measurements.\"},{\"question\":\"Which features are most significant for predicting obesity across age and gender groups?\",\"answer\":\"Triglycerides, ALT (SGPT), glycated hemoglobin, and uric acid are highlighted as the four most significant features across all age-gender groups.\"}]","Age-specific risk factors for the prediction of obesity using a machine learning approach | 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is the main goal of the study?","Question",{"text":76,"@type":77},"To identify risk factors that predict obesity using machine learning classifiers and to determine the algorithm with the best accuracy among the tested models.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data source and measurements are used for prediction?",{"text":81,"@type":77},"The study uses KNHANES data and evaluates predictors derived from blood tests and blood pressure measurements.",{"name":83,"@type":74,"acceptedAnswer":84},"Which features are most significant for predicting obesity across age and gender groups?",{"text":85,"@type":77},"Triglycerides, ALT (SGPT), glycated hemoglobin, and uric acid are highlighted as the four most significant features across all age-gender 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