ArticleScientific reports2023
Machine learning-based obesity classification considering 3D body scanner measurements.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 30 citations in OpenAlex.
- Duplicate-Aware Internal Validation of Machine-Learning Models for Classifying a Tanita BIA-Derived High-Adiposity Phenotype Using Simple Anthropometric Predictors.Journal of clinical medicine · 2026Article
- Automatic classification of female body shape using 3D anthropometric scan data.Scientific reports · 2026Article
- Modernization of Nutritional Assessment in Population Surveys: Integrating Anthropometry, Body Composition, and Biomarkers in the Digital Era.Current nutrition reports · 2026Review
- 3D body shape-based phenotyping for precision risk stratification of metabolic syndrome.Lipids in health and disease · 2026Article
- Voxel-based Deep Regression for Enhanced Body Composition Estimation from 3D Body Scans.SN computer science · 2026Article
- 3D Body Scanning-Derived Normative Values of Appendicular Circumferences: A Novel Tool for Sarcopenia Screening in Chinese Adults.Journal of cachexia, sarcopenia and muscle · 2025Article
- Lifestyle data-based multiclass obesity prediction with interpretable ensemble models incorporating SHAP and LIME analysis.Scientific reports · 2025Article
- Equations for Assessing Body Composition by Ultrasound in Older Adults: A Narrative Review.Healthcare (Basel, Switzerland) · 2025Review
- Weight Management for Fertility-Preservation Therapy in Endometrial Cancer: Opportunities and Challenges.Current oncology reports · 2025Review
- Deep Learning-Based Body Shape Clustering Analysis Using 3D Body Scanner: Application of Transformer Algorithm.Iranian journal of public health · 2025Article
- The challenges of assessing adiposity in a clinical setting.Nature reviews. Endocrinology · 2024Review
- Review
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Authors and funding
5 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
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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