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Updated: Sep 16, 2025
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Construction and evaluation of a height prediction model for children with growth disorders treated with recombinant
Feng Zhu1, Anle Wu1, Lingling Chen1
1Department of Child Healthcare, Wenzhou People's Hospital/Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China.
Insights
Machine learning models effectively predict height gain in children undergoing growth hormone therapy. These tools aid personalized treatment by analyzing clinical data for better outcomes.
Area of Science:
- Pediatric endocrinology
- Biostatistics
- Machine learning in healthcare
Background:
- Height gain in children with growth disorders on recombinant human growth hormone (rhGH) therapy exhibits significant variability.
- Predicting treatment response is crucial for tailoring individualized therapeutic strategies.
Purpose of the Study:
- To develop and validate a predictive model using clinical data for early height growth response assessment in children with growth disorders undergoing rhGH therapy.
Main Methods:
- 786 children were analyzed, split into derivation (N=551) and test (N=235) cohorts.
- Machine learning models (logistic regression, decision tree, random forest, XGBoost, LightGBM, MLP) were built and evaluated using AUROC, AUPRC, and accuracy.
- Key input variables included chronological age, HSDS, BSDS, IGF-1, and BA-CA.
Main Results:
- Random forest and MLP models demonstrated superior performance.
- The random forest model achieved an AUROC of 0.9114 and AUPRC of 0.8825.
- MLP achieved high accuracy (0.8468), precision (0.8208), specificity (0.8583), and F1 score (0.8246). Chronological age, BA-CA, HSDS, and BSDS were most influential.
Conclusions:
- Machine learning models, particularly random forest and MLP, effectively predict height gain in children receiving rhGH therapy.
- These models support personalized treatment strategies for growth disorders.
- Enhancing the interpretability of high-performing models like MLP is recommended for clinical integration.
Background:
Height gain in children with growth disorders undergoing recombinant human growth hormone (rhGH) therapy shows considerable variability. Predicting treatment outcomes is essential for optimizing individualized treatment strategies.
Objective:
To develop and evaluate a predictive model using clinical data to assess early height growth response in children with growth disorders undergoing rhGH therapy.
Methods:
A total of 786 children were included, randomly split into a derivation cohort (N = 551) and a test cohort (N = 235). Multiple machine learning models were built in the derivation cohort, including logistic regression, decision tree, random forest, XGBoost, LightGBM, and multilayer perceptron (MLP). Model performance was evaluated in the test cohort using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and accuracy metrics. Input variables included chronological age, height standard deviation score (HSDS), body mass index standard deviation score (BSDS), IGF-1, and the difference between bone age and chronological age (BA-CA).
Results:
The random forest and MLP models performed best. The random forest model achieved an AUROC of 0.9114 and an AUPRC of 0.8825. The MLP model showed accuracy, precision, specificity, and F1 scores of 0.8468, 0.8208, 0.8583, and 0.8246, respectively. Chronological age, BA-CA, HSDS, and BSDS were the most influential variables. The decision tree identified HSDS ≥ -0.72 as the primary split point.
Conclusion:
Machine learning models, especially random forest and MLP, predict height gain effectively in children receiving rhGH therapy, aiding personalized treatment. Despite MLP's strong performance, its "black-box" nature may limit clinical adoption. Future work should focus on enhancing model interpretability.
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