[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124597-en":3,"doc-seo-124597-105":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},124597,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",7,"Healthcare","Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records","Obesity is a critical health condition that reduces quality of life and overall well-being, and it is strongly linked to serious comorbidities such as cardiac diseases, diabetes, hypertension, and some cancer types. Preventing or reversing obesity requires accurate estimation and detection across obesity classes. An AI-enabled machine learning approach is applied to real electronic health records, covering underweight, normal weight, overweight, and obesity types I–III using features spanning physical condition, diet, lifestyle, and transportation. XGB outperforms existing methods with reported performance rates up to 98.5%–99.6% across tested scenarios.","Tech Science Press  \nDOI: 10.32604/csse.2023.035687  \nArticle  \nMachine-Learning-Enabled Obesity Level Prediction Through Electronic  \nHealth Records  \nSaeed Ali Alsareii1 , Muhammad Awais2 , *, Abdulrahman Manaa Alamri1 , Mansour Yousef AlAsmari1 ,  \nMuhammad Irfan3 , Mohsin Raza2 and Umer Manzoor4  \n1 Department of Surgery, College of Medicine, Najran University, Najran, 61441, Saudi Arabia  \n2 Department of Computer Science, Edge Hill University, St Helens Rd, Ormskirk, L39 4QP, UK  \n3 Electrical Engineering Department, College of Engineering, Najran University, Najran, 61441, Saudi Arabia  \n4 Department of Computer Science, Aston University, Birmingham, B4 7ET, UK  \n*[Corresponding Author: Muhammad Awais. Email: mawais102@gmail.com](Corresponding Author: Muhammad Awais. Email: mawais102@gmail.com)  \nReceived: 31 August 2022; Accepted: 02 February 2023  \nAbstract: Obesity is a critical health condition that severely affects an individual’s quality oflife and well-being. The occurrence ofobesity is strongly associated with extreme health conditions, such as cardiac diseases, diabetes, hypertension, and some types of cancer. Therefore, it is vital to avoid obesity and or reverse its occurrence. Incorporating healthy food habits and an active lifestyle can help to prevent obesity. In this regard, artificial intelligence (AI) can play an important role in estimating health conditions and detecting obesity and its types. This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset. This dataset is in the form of electronic health records (EHR) containing data on several aspects of daily living, such as dietary habits, physical conditions, and lifestyle variables for various participants with different health conditions (underweight, normal, overweight, and obesity type I, II and III), expressed in terms of a variety of features or parameters, such as physical condition, food intake, lifestyle and mode of transportation. Three classifiers, i.e., eXtreme gradient boosting classifier(XGB), support vector machine(SVM), and artificial neural network (ANN), are implemented to detect the status of several conditions, including obesity types. The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods, achieving overall  \nperformance rates of 98 . 5% and 99 .6% in the scenarios explored.  \nKeywords: Artificial intelligence; obesity; machine learning; extreme gradient boosting classifier; support vector machine; artificial neural network;  \nelectronic health records; physical activity; obesity levels  \n1 Introduction  \nObesity is the leading cause of a variety of health issues, both alone and in conjunction with other conditions [1,2] . Obesity is directly linked to several diseases, including respiratory problems,  \nThis work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \n3716 CSSE, 2023, vol.46, no.3  \ndiabetes, heart issues, some cancer types, cholesterol, and osteoarthritis [3,4] . The adverse effects of excess weight can take ten years or more. Increased weight is also a strong indicator of shorter longevity.  \nDifferent methods are used to determine the appropriate weight and identify instances of obesity and overweight. Excess fat in the body is the main reason for obesity. Body fat is evaluated with ease using a relationship considering both the height and weight of a person. In evaluation, it is assumed that the importance of individuals of the same size is primarily changed depending on body fat. One of the most commonly used fat quantification mechanisms is the body mass index (BMI), whereas other techniques, such as waist circumference, skinfold thickness, and bioimpedance, are also considered. Among different classification grades for obesity, BMI readings provide es","cbCaiggw4dTmLJYi","https://ap.wps.com/l/cbCaiggw4dTmLJYi","pdf",533369,1,14,"English","en",105,"# Introduction\n## Obesity risk and measurement methods\n## AI and machine learning for obesity detection","[{\"question\":\"What data source is used for obesity level prediction in the study?\",\"answer\":\"The study uses electronic health records (EHR) that include dietary habits, physical conditions, and lifestyle variables for participants across multiple health categories.\"},{\"question\":\"Which machine learning classifiers are used to detect obesity statuses?\",\"answer\":\"Three classifiers are implemented: eXtreme gradient boosting (XGB), support vector machine (SVM), and artificial neural network (ANN).\"},{\"question\":\"How does the proposed method perform compared with existing approaches?\",\"answer\":\"The proposed XGB-based system shows better overall performance, reporting rates of about 98.5% and 99.6% in the explored scenarios.\"}]","Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records | 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data source is used for obesity level prediction in the study?","Question",{"text":75,"@type":76},"The study uses electronic health records (EHR) that include dietary habits, physical conditions, and lifestyle variables for participants across multiple health categories.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning classifiers are used to detect obesity statuses?",{"text":80,"@type":76},"Three classifiers are implemented: eXtreme gradient boosting (XGB), support vector machine (SVM), and artificial neural network (ANN).",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method perform compared with existing approaches?",{"text":84,"@type":76},"The proposed XGB-based system shows better overall performance, reporting rates of about 98.5% and 99.6% in the explored 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