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Predicting risk of overweight or obesity in Chinese preschool-aged children using artificial intelligence techniques

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Abstract

Objectives

We adopted the machine-learning algorithms and deep-learning sequential model to determine and optimize most important factors for overweight and obesity in Chinese preschool-aged children.

Methods

This is a cross-sectional survey conducted in 2020 at Beijing and Tangshan. Using a stratified cluster random sampling strategy, children aged 3-6 years were enrolled. Data were analyzed using the PyCharm and Python.

Results

A total of 9478 children were eligible for inclusion, including 1250 children with overweight or obesity. All children were randomly divided into the training group and testing group at a 6:4 ratio. After comparison, support vector machine (SVM) outperformed the other algorithms (accuracy: 0.9457), followed by gradient boosting machine (GBM) (accuracy: 0.9454). As reflected by other 4 performance indexes, GBM had the highest F1 score (0.7748), followed by SVM with F1 score at 0.7731. After importance ranking, the top 5 factors seemed sufficient to obtain descent performance under GBM algorithm, including age, eating speed, number of relatives with obesity, sweet drinking, and paternal education. The performance of the top 5 factors was reinforced by the deep-learning sequential model.

Conclusions

We have identified 5 important factors that can be fed to GBM algorithm to better differentiate children with overweight or obesity from the general children, with decent prediction performance.

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Author information

Author notes

  1. These authors contributed equally: Qiong Wang, Min Yang

Authors and Affiliations

  1. Graduate School, Beijing University of Chinese Medicine, Beijing, China

    Qiong Wang, Min Yang, Bo Pang, Mei Xue & Yicheng Zhang

  2. Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China

    Qiong Wang, Min Yang, Bo Pang, Mei Xue, Yicheng Zhang & Zhixin Zhang

  3. International Medical Services, China-Japan Friendship Hospital, Beijing, China

    Zhixin Zhang

  4. Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China

    Wenquan Niu

Authors
  1. Qiong Wang
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  2. Min Yang
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  4. Mei Xue
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  5. Yicheng Zhang
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Contributions

Z.Z. planned and designed the study and directed its implementation. Z.Z. drafted the protocol. Q.W., M.Y., B.P., M.X., and Y.Z. obtained statutory and ethics approvals. Q.W. and M.Y. contributed to data acquisition. Q.W. and W.N. conducted statistical analyses. Q.W., M.Y., B.P., and M.X., did the data preparation and quality control. Q.W. and W.N. wrote the manuscript. All authors read and approved the final manuscript prior to submission.

Corresponding authors

Correspondence to Zhixin Zhang or Wenquan Niu.

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The authors declare no competing interests.

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Wang, Q., Yang, M., Pang, B. et al. Predicting risk of overweight or obesity in Chinese preschool-aged children using artificial intelligence techniques. Endocrine 77, 63–72 (2022). https://doi.org/10.1007/s12020-022-03072-1

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  • DOI: https://doi.org/10.1007/s12020-022-03072-1

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