Barzinji, Ala Othman, Ma, Chaoying, Du, Wencai and Ma, Jixin ORCID: https://orcid.org/0000-0001-7458-7412 (2021) A machine learning approach to predict the trend of obesity prevalence at a global level. In: 2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD). IEEE Xplore . Institute of Electrical and Electronics Engineers (IEEE), Piscataway, New Jersey, pp. 25-30. ISBN 978-1728176819 (doi:10.1109/BCD51206.2021.9581579)

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Abstract

Excessive weight is associated with adverse health risks. Understanding the global trends in Obesity, in children and adolescents, is paramount to impede the increasing rate of global Obesity prevalence. Different machine learning models were used to predict Obesity prevalence, in children and adolescents, at a global level. This paper presents a novel approach to predict Obesity beyond 2030 using machine learning. The data was derived from a global population-based survey in 2015. In the main study, we applied machine learning models to predict the exponential rise in Obesity prevalence across the world in 2030, 2040, and 2050. In the second study, we further calculated the Obesity prevalence rates according to Socio-Demographic Index (SDI). We obtained promising results with model prediction accuracies of up to 99% R2 for the main study, and up to 92% R2 for the SDI study.

Item Type:	Conference Proceedings
Title of Proceedings:	2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD)
Additional Information:	Date of Conference: 13-15 September 2021. Conference Location: Zhuhai, China.
Uncontrolled Keywords:	obesity, pediatrics, computational modeling, linear regression, machine learning, predictive models, tools, children and adolescent obesity, childhood obesity prediction
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	29 Jun 2026 16:09
URI:	https://gala.gre.ac.uk/id/eprint/53892

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