Optimizing Future Predictions in Children's Health: Implementing OGPA-enhanced Deep Learning for Precise Child Height Forecasting in the Social Media Age

In recent years, the issue of children's height has garnered widespread attention on social media. Social media platforms serve as pivotal communication channels for parents, doctors, educators, and researchers, with children's height, a crucial health indicator, becoming one of the hot topics. Inaccurate prediction methods might mislead the public, resulting in parents harboring erroneous expectations regarding their children's future height. Such misplaced expectations may culminate in unwarranted worries or pressure. In pursuit of devising an accurate height prediction model, this paper thoroughly leverages a substantial data sample obtained from the physical health examinations of primary and secondary school students in Zhejiang Province, as well as continuous observation samples provided by the Zhejiang Provincial Bone Age Research Center, to delve deeply into the issue of height prediction in children and adolescents. In this research, we have developed a lightweight neural net-work model suitable for particle swarm optimization to predict children’s stage-wise height. When the difference between the actual and predicted values is within ±2cm, the prediction accuracy for boys reached 86.67%, and for girls, it was 85.32%, with an RMSE of 1.3503.