Artificial Intelligence for Pediatric Height Prediction Using Large-Scale Longitudinal Body Composition Data
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915234711601152 |
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| author | Chun, Dohyun Jung, Hae Woon Kang, Jongho Jang, Woo Young Kim, Jihun |
| author_facet | Chun, Dohyun Jung, Hae Woon Kang, Jongho Jang, Woo Young Kim, Jihun |
| contents | This study developed an accurate artificial intelligence model for predicting future height in children and adolescents using anthropometric and body composition data from the GP Cohort Study (588,546 measurements from 96,485 children aged 7-18). The model incorporated anthropometric measures, body composition, standard deviation scores, and growth velocity parameters, with performance evaluated using RMSE, MAE, and MAPE. Results showed high accuracy with males achieving average RMSE, MAE, and MAPE of 2.51 cm, 1.74 cm, and 1.14%, and females showing 2.28 cm, 1.68 cm, and 1.13%, respectively. Explainable AI approaches identified height SDS, height velocity, and soft lean mass velocity as crucial predictors. The model generated personalized growth curves by estimating individual-specific height trajectories, offering a robust tool for clinical decision support, early identification of growth disorders, and optimization of growth outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence for Pediatric Height Prediction Using Large-Scale Longitudinal Body Composition Data Chun, Dohyun Jung, Hae Woon Kang, Jongho Jang, Woo Young Kim, Jihun Quantitative Methods Machine Learning 62P10, 68T05 This study developed an accurate artificial intelligence model for predicting future height in children and adolescents using anthropometric and body composition data from the GP Cohort Study (588,546 measurements from 96,485 children aged 7-18). The model incorporated anthropometric measures, body composition, standard deviation scores, and growth velocity parameters, with performance evaluated using RMSE, MAE, and MAPE. Results showed high accuracy with males achieving average RMSE, MAE, and MAPE of 2.51 cm, 1.74 cm, and 1.14%, and females showing 2.28 cm, 1.68 cm, and 1.13%, respectively. Explainable AI approaches identified height SDS, height velocity, and soft lean mass velocity as crucial predictors. The model generated personalized growth curves by estimating individual-specific height trajectories, offering a robust tool for clinical decision support, early identification of growth disorders, and optimization of growth outcomes. |
| title | Artificial Intelligence for Pediatric Height Prediction Using Large-Scale Longitudinal Body Composition Data |
| topic | Quantitative Methods Machine Learning 62P10, 68T05 |
| url | https://arxiv.org/abs/2504.06979 |