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Adult height prediction using the growth curve comparison method

Author

Listed:
  • Miha Mlakar
  • Anton Gradišek
  • Mitja Luštrek
  • Gregor Jurak
  • Maroje Sorić
  • Bojan Leskošek
  • Gregor Starc

Abstract

Understanding the growth pattern is important in view of child and adolescent development. Due to different tempo of growth and timing of adolescent growth spurt, individuals reach their adult height at different ages. Accurate models to assess the growth involve intrusive radiological methods whereas the predictive models based solely on height data are typically limited to percentiles and therefore rather inaccurate, especially during the onset of puberty. There is a need for more accurate non-invasive methods for height prediction that are easily applicable in the fields of sports and physical education, as well as in endocrinology. We developed a novel method, called Growth Curve Comparison (GCC), for height prediction, based on a large cohort of > 16,000 Slovenian schoolchildren followed yearly from ages 8 to 18. We compared the GCC method to the percentile method, linear regressor, decision tree regressor, and extreme gradient boosting. The GCC method outperformed the predictions of other methods over the entire age span both in boys and girls. The method was incorporated into a publicly available web application. We anticipate our method to be applicable also to other models predicting developmental outcomes of children and adolescents, such as for comparison of any developmental curves of anthropometric as well as fitness data. It can serve as a useful tool for assessment, planning, implementation, and monitoring of somatic and motor development of children and youth.

Suggested Citation

  • Miha Mlakar & Anton Gradišek & Mitja Luštrek & Gregor Jurak & Maroje Sorić & Bojan Leskošek & Gregor Starc, 2023. "Adult height prediction using the growth curve comparison method," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-13, February.
  • Handle: RePEc:plo:pone00:0281960
    DOI: 10.1371/journal.pone.0281960
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    References listed on IDEAS

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    1. Takao Shohoji & Kouji Kanefuji & Takahiro Sumiya & Tao Qin, 1991. "A prediction of individual growth of height according to an empirical Bayesian approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(4), pages 607-619, December.
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