Prediction of Excess Body Fat in Adolescents Using Neural Networks

Araujo, Claudyane; Azoubel, Luana Monteiro Anaisse; Barros, Allan Kardec; Carlos, Marchena; Sousa, Nilviane P S; Pereira, Cindy Lima; Rosa, Claudia Regina Arrais; Santana, Ewaldo Éder Carvalho

doi:10.1145/3487027.3487037

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Fujihara et al (2023) and Xu et al (2023) have investigated the prediction of body weight and body fat percentage using machine learning techniques. Furthermore, Kupusinac et al (2014), Shao et al (2023), Araujo et al (2021), Ferdowsy and Pourghasemi (2021), and Singh and Tawfik (2020) have explored various aspects of body fat prediction and related obesity risk assessment. These works collectively contribute to the growing body of knowledge in the field and illustrate the versatility of machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Predicting Body Fat Percentage: A Machine Learning Approach

SANTOS

2023

Preprint

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Accurate estimation of body fat percentage is essential for various health and fitness applications. Traditional methods for measuring body fat, such as underwater weighing, can be costly and inconvenient. In this study, we apply machine learning techniques to predict body fat percentage using easily accessible body measurements and data. The results highlight the effectiveness of regression models in estimating body fat, with Linear Regression, Ridge Regression, and Bayesian Ridge models achieving R-squared scores of approximately 74%, 73%, and 73%, respectively. These models provide a practical and cost-effective solution for individuals and professionals seeking reliable body fat estimates.

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