4.7 Article

Machine learning-based obesity classification considering 3D body scanner measurements

Journal

SCIENTIFIC REPORTS
Volume 13, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41598-023-30434-0

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Obesity is a serious health concern that can lead to various diseases. The widely used measure of obesity, BMI, does not accurately classify obesity and overlooks individual body type. To address this limitation, we conducted a study using 3D measurements of the human body, focusing on Korean subjects. By collecting 3D body scans, Dual-energy X-ray absorptiometry, and Bioelectrical Impedance Analysis data from 160 Korean subjects, we developed a machine learning-based obesity classification framework and compared its performance with BMI and BIA. Our proposed model demonstrated higher accuracy in obesity classification than BMI and BIA, and can be utilized for obesity management through 3D body scans.
Obesity can cause various diseases and is a serious health concern. BMI, which is currently the popular measure for judging obesity, does not accurately classify obesity; it reflects the height and weight but ignores the characteristics of an individual's body type. In order to overcome the limitations of classifying obesity using BMI, we considered 3-dimensional (3D) measurements of the human body. The scope of our study was limited to Korean subjects. In order to expand 3D body scan data clinically, 3D body scans, Dual-energy X-ray absorptiometry, and Bioelectrical Impedance Analysis data was collected pairwise for 160 Korean subjects. A machine learning-based obesity classification framework using 3D body scan data was designed, validated through Accuracy, Recall, Precision, and F1 score, and compared with BMI and BIA. In a test dataset of 40 people, BMI had the following values: Accuracy: 0.529, Recall: 0.472, Precision: 0.458, and F1 score: 0.462, while BIA had the following values: Accuracy: 0.752, Recall: 0.742, Precision: 0.751, and F1 score: 0.739. Our proposed model had the following values: Accuracy: 0.800, Recall: 0.767, Precision: 0.842, and F1 score: 0.792. Thus, our accuracy was higher than BMI as well as BIA. Our model can be used for obesity management through 3D body scans.

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