The prediction of children's adult height is a common procedure in childhood endocrinology. Through the prediction of children's adult height, it is possible to find abnormalities in children's growth and development. Many jobs in today's society have certain requirements for height, so the accuracy of children adulthood height prediction is important for children. Current methods for predicting adult height of children have some shortcomings such as inaccurate accuracy. To deal with these problems, this paper analyzes the data collected by the Chinese children and adolescents' physical and growth health projects in primary and secondary schools in Zhejiang Province, and proposes a method for predicting adult height based on back propagation neural network (BPNN) with the body composition of children and adolescents as input. Since the BP algorithm has the risk of falling into local optimization, and we propose LSALO-BP model that incorporates the ant lion optimizer (LSALO) into the BP algorithm as location strategy to avoid local optimization. The improvements achieved by the ant lion algorithm are mainly reflected in: improving the ant's walk mode, and enhancing the global search ability of the LSALO algorithm. The comparison experiment of 10 benchmark functions proves the feasibility and effectiveness of the location strategy. The LSALO-BP model is applied to the prediction of adult height of children and adolescents. The experimental results show that compared with other models, the LSALO-BP prediction model has increased the prediction accuracy by 6.67%~16.08% for boys and 4.67%~6.6% for girls, which can more accurately predict the adult height of children and adolescents.
Predicting the adult height of children accurately has great social value for the selection of outstanding athlete as well as early detection of children’s growth disorders. Currently, the mainstream method used to predict adult height in China has three problems: its standards are not uniform; it is stale for current Chinese children; its accuracy is not satisfactory. This article uses the data collected by the Chinese Children and Adolescents’ Physical Fitness and Growth Health Project in Zhejiang primary and secondary schools. We put forward a new multidimensional and high-precision youth growth curve prediction model, which is based on multilayer perceptron. First, this model uses multidimensional growth data of children as predictors and then utilizes multilayer perceptron to predict the children’s adult height. Second, we find the Table of Height Standard Deviation of Chinese Children and fit the data of zero standard deviation to obtain the curve. This curve is regarded as Chinese children’s mean growth curve. Third, we use the least-squares method and the mean curve to calculate the individual growth curve. Finally, the individual curve can be used to predict children’s state height. Experimental results show that this adult height prediction model’s accuracy (between 2 cm) of boys and girls reached 90.20% and 88.89% and the state height prediction accuracy reached 77.46% and 74.93%. Compared with Bayley–Pinneau, the adult height prediction is improved 19.61% for boys and 13.33% for girls. Compared with BoneXpert, the adult height prediction is improved 25.49% for boys and 6.67% for girls. Compared with the method based on the bone age growth map, the adult height prediction is improved 15.69% for boys and 24.45% for girls.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.