Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

作     者：Saeed Ali Alsareii Muhammad Awais Abdulrahman Manaa Alamri Mansour Yousef AlAsmari Muhammad Irfan Mohsin Raza Umer Manzoor 

作者机构：Department of SurgeryCollege of MedicineNajran UniversityNajran61441Saudi Arabia Department of Computer ScienceEdge Hill UniversitySt Helens RdOrmskirkL394QPUK Electrical Engineering DepartmentCollege of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Computer ScienceAston UniversityBirminghamB47ETUK 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第9期

页      面：3715-3728页

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education Kingdom of Saudi Arabia for this research through a grant(NU/IFC/ENT/01/020)under the Institutional Funding Committee at Najran University Kingdom of Saudi Arabia 

主　　题：Artificial intelligence obesity machine learning extreme gradient boosting classifier support vector machine artificial neural network electronic health records physical activity obesity levels 

摘      要：Obesity is a critical health condition that severely affects an individual’s quality of life *** occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of ***,it is vital to avoid obesity and or reverse its *** healthy food habits and an active lifestyle can help to prevent *** this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its *** study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life *** 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 *** 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 *** findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.