Machine-learning-enabled obesity level prediction through electronic health records

Alsareii, Saeed Ali, Awais, Muhammad ORCID: https://orcid.org/0000-0001-6421-9245, Alamri, Abdulrahman Manaa, AlAsmari, Mansour Yousef, Irfan, Muhammad, Raza, Mohsin and Manzoor, Umer (2023) Machine-learning-enabled obesity level prediction through electronic health records. Computer Systems Science and Engineering, 46 (3). pp. 3715-3728. ISSN 0267-6192

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Abstract

Obesity is a critical health condition that severely affects an individual’s quality of life and well-being. The occurrence of obesity 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 AI-enabled 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 performance rates of 98.5% and 99.6% in the scenarios explored.

Item Type:	Article
Uncontrolled Keywords:	sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI
Depositing User:	LivePure Connector
Date Deposited:	17 Oct 2023 00:44
Last Modified:	21 Dec 2024 01:08
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93296
DOI:	10.32604/csse.2023.035687

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