Improving the Accuracy of Adult Height Prediction With Exploiting Multiple Machine Learning Models According to the Distribution of Parental Height

Abstract

Grade schoolers and teenagers wonder how tall they will be, as there is a tendency to prefer taller stature for many years. Child's height growth is one of the continuous interests of the parents from the past to the present for many reasons, not only their children's outer beauty but also health status of children. Pediatricians also want to make sure a child is growing as expected because the height growth of children is an important indicator for monitoring a child's nutrition and diseases. In many previous studies, adult height prediction method using growth curves is used widely. Unfortunately, growth curves are based on longitudinal cohort studies which are very challenging to make themselves. That's why it is hard to find the related studies for certain ethnic group. In this study, we collected 2,687 Korean height data including parental heights and children's heights by ourselves in the same format as Galton's Height data at 1880s in the United Kingdom. Then, we focus on the influence of parental height on child's height conducting various analysis comparing Galton's and Korean height data. Especially, we find out the linearity of child's height varies depending on the combination of each parental height through visualization analysis. Finally, we propose our method of deploying the best among various machine learning techniques according to the combination of parental height. The combination is based on distribution of each parental height. And it outperforms achieving RMSE under 3.5 compared to single machine learning models which cannot achieve RMSE even under 4.0. It will be a simple and good application for many of pediatricians and parents who care a lot about their children's height growth.