Childhood Obesity Prediction: Algorithms, Datasets, and Taxonomies
Pages 44 - 48
Abstract
Childhood obesity is a significant health concern impacting children and adolescents. The excess weight often initiates health issues such as diabetes, high blood pressure, and elevated cholesterol levels. Moreover, childhood obesity can contribute to diminished self-esteem. Machine learning models have been proposed for predicting childhood obesity in a variety of studies. Additionally, these studies aim to discover important factors leading to obesity for the construction of effective intervention tools. The objective of this survey study is to perform a thorough evaluation of the most recent research concerning machine learning models for predicting obesity or identifying risk factors. Furthermore, this study provides a consistent view of the limits of existing work.
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Published In
May 2024
367 pages
ISBN:9798400716928
DOI:10.1145/3660853
Copyright © 2024 ACM.
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Published: 23 June 2024
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AICCONF '24
AICCONF '24: Cognitive Models and Artificial Intelligence Conference
May 25 - 26, 2024
İstanbul, Turkiye
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