Improving the Accuracy of Adult Height Prediction With Exploiting Multiple Machine Learning Models According to the Distribution of Parental Height
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 bea...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10227284/ |
| _version_ | 1797692201519546368 |
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| author | Ji-Sung Park Dong-Ho Lee |
| author_facet | Ji-Sung Park Dong-Ho Lee |
| author_sort | Ji-Sung Park |
| collection | DOAJ |
| description | 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 conduct. 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. |
| first_indexed | 2024-03-12T02:24:11Z |
| format | Article |
| id | doaj.art-886521deb2644e72bd6a69948d638426 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T02:24:11Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-886521deb2644e72bd6a69948d6384262023-09-05T23:00:46ZengIEEEIEEE Access2169-35362023-01-0111914549147110.1109/ACCESS.2023.330773110227284Improving the Accuracy of Adult Height Prediction With Exploiting Multiple Machine Learning Models According to the Distribution of Parental HeightJi-Sung Park0https://orcid.org/0000-0001-8947-6387Dong-Ho Lee1https://orcid.org/0000-0003-0305-9182Department of Applied Artificial Intelligence (Major in Bio Artificial Intelligence), Hanyang University, Seoul, South KoreaDepartment of Artificial Intelligence, Hanyang University ERICA Campus, Ansan, South KoreaGrade 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 conduct. 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.https://ieeexplore.ieee.org/document/10227284/Child's adult height prediction (AHP)data analysismachine learninghealthcare |
| spellingShingle | Ji-Sung Park Dong-Ho Lee Improving the Accuracy of Adult Height Prediction With Exploiting Multiple Machine Learning Models According to the Distribution of Parental Height IEEE Access Child's adult height prediction (AHP) data analysis machine learning healthcare |
| title | Improving the Accuracy of Adult Height Prediction With Exploiting Multiple Machine Learning Models According to the Distribution of Parental Height |
| title_full | Improving the Accuracy of Adult Height Prediction With Exploiting Multiple Machine Learning Models According to the Distribution of Parental Height |
| title_fullStr | Improving the Accuracy of Adult Height Prediction With Exploiting Multiple Machine Learning Models According to the Distribution of Parental Height |
| title_full_unstemmed | Improving the Accuracy of Adult Height Prediction With Exploiting Multiple Machine Learning Models According to the Distribution of Parental Height |
| title_short | Improving the Accuracy of Adult Height Prediction With Exploiting Multiple Machine Learning Models According to the Distribution of Parental Height |
| title_sort | improving the accuracy of adult height prediction with exploiting multiple machine learning models according to the distribution of parental height |
| topic | Child's adult height prediction (AHP) data analysis machine learning healthcare |
| url | https://ieeexplore.ieee.org/document/10227284/ |
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