Identification and characterization of factors associated with short stature and pre-shortness in Chinese preschool-aged children

[Attaching great importance to the scientific assessment of short stature in children]

Short stature is a common physical developmental abnormality in children. Without timely and accurate diagnosis, as well as early intervention, it can impose a heavy burden on the children and their families. There are numerous causes for short stature, and the diagnostic process essentially involves identifying its underlying causes. Based on a thorough understanding of the regular patterns of child physical development and the characteristics of individuals at high risk of short stature, a scientific definition of short stature needs to be established, along with standardized diagnostic and treatment protocols, to achieve early diagnosis or referral for short stature. Furthermore, it is necessary to enhance scientific awareness of short stature among parents and primary care pediatricians, in order to avoid over-treatment, missed diagnoses, and misdiagnoses arising from "misconceptions", and to improve the scientific assessment of short stature.

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

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.