Child’s Target Height Prediction Evolution

Cordeiro, João Rala; Postolache, Octavian; Ferreira, Joao C Ferreira or Joao

doi:10.3390/app9245447

Cited by 5 publications

(8 citation statements)

References 34 publications

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“…In boys the average prediction error of the XBG method was around 3 mm larger than of the GCC method, but this difference declined towards adult height. Our identified prediction errors of this method were, nevertheless, much smaller than the reported errors in a study utilizing parental height [ 34 ], which reported almost 60 mm prediction error.…”

Section: Discussioncontrasting

confidence: 74%

“…Another recent study by Rativa et al [ 32 ] used support vector regression, Gaussian process regression and artificial neural networks, to estimate height and weight from anthropometric measurements. Both aforementioned studies predicted height of the already adult individuals and their results could be used in forensics, textile technology, security and health care, but it wasn’t until recently that the first attempts to use ML for adult height prediction of children from childhood height [ 33 ] or parental height [ 34 ] were published.…”

Section: Introductionmentioning

confidence: 99%

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Adult height prediction using the growth curve comparison method

et al. 2023

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Understanding the growth pattern is important in view of child and adolescent development. Due to different tempo of growth and timing of adolescent growth spurt, individuals reach their adult height at different ages. Accurate models to assess the growth involve intrusive radiological methods whereas the predictive models based solely on height data are typically limited to percentiles and therefore rather inaccurate, especially during the onset of puberty. There is a need for more accurate non-invasive methods for height prediction that are easily applicable in the fields of sports and physical education, as well as in endocrinology. We developed a novel method, called Growth Curve Comparison (GCC), for height prediction, based on a large cohort of > 16,000 Slovenian schoolchildren followed yearly from ages 8 to 18. We compared the GCC method to the percentile method, linear regressor, decision tree regressor, and extreme gradient boosting. The GCC method outperformed the predictions of other methods over the entire age span both in boys and girls. The method was incorporated into a publicly available web application. We anticipate our method to be applicable also to other models predicting developmental outcomes of children and adolescents, such as for comparison of any developmental curves of anthropometric as well as fitness data. It can serve as a useful tool for assessment, planning, implementation, and monitoring of somatic and motor development of children and youth.

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Section: Discussioncontrasting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Adult height prediction using the growth curve comparison method

et al. 2023

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“…Sunghyeon Choi forecasted solar energy output by employing RF, XGBoost, and LightGBM models [ 26 ]. Moreover, J.Cordeiro, O. Postolache, and J. Ferreira used the XGBoost model and the LightGBM model to predict the height of children [ 27 ]. Accordingly, in the modeling phase, we adopted the XGBoost and LightGBM to have the benefits of the features of our PAN-LDA model.…”

Section: Related Workmentioning

confidence: 99%

“… Reference Year Base model Time series prediction Data set [ 28 ] 2021 SVM, LR, Multi-layer perceptron, RF Covid-19 pandemic cumulative case forecasting Data of COVID-19 between January 20, 2020, and September 18, 2020, for the USA, Germany, and global was obtained from the World Health Organization website. [ 17 ] 2020 LR, SVM, ANN, RF, XGBoost, LightGBM Prediction of peak demand days of cardiovascular disease(CVD) admissions Health Information Center of Sichuan Province, China: the daily number of admissions of CVD patients in hospital Chengdu Meteorological Monitoring Database: Meteorological data China National Environmental Monitoring Cente: air pollutants data [ 25 ] 2020 XGBoost, CatBoost, LightGBM Prediction of problems in data mining of credit scoring domain Home Credit Default Risk from Kaggle Challenge [ 26 ] 2020 RF, XGBoost, LightGBM Photovoltaic Forecasting Data of a Photovoltaic plant in South Korea [ 18 ] 2020 LightGBM Cryptocurrency price trend Daily trading data from https://www.investing.com/ [ 29 ] 2020 XGBoost, ARIMA Hemorrhagic fever with renal syndrome Monthly hemorrhagic fever with renal syndrome incidence data from 2004 to 2018 from the official website of the National Health Commission of the People's Republic of China [ 27 ] …”

Section: Related Workmentioning

confidence: 99%

PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning

Gupta

Katarya

2021

Computers in Biology and Medicine

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The recent outbreak of novel Coronavirus disease or COVID-19 is declared a pandemic by the World Health Organization (WHO). The availability of social media platforms has played a vital role in providing and obtaining information about any ongoing event. However, consuming a vast amount of online textual data to predict an event's trends can be troublesome. To our knowledge, no study analyzes the online news articles and the disease data about coronavirus disease. Therefore, we propose an LDA-based topic model, called PAN-LDA (Pandemic-Latent Dirichlet allocation), that incorporates the COVID-19 cases data and news articles into common LDA to obtain a new set of features. The generated features are introduced as additional features to Machine learning(ML) algorithms to improve the forecasting of time series data. Furthermore, we are employing collapsed Gibbs sampling (CGS) as the underlying technique for parameter inference. The results from experiments suggest that the obtained features from PAN-LDA generate more identifiable topics and empirically add value to the outcome.

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“…Recently, there is an approach using height data and machine learning techniques [25][26][27]. The study [25] analyze Galton's height data and predict adult height using various machine learning techniques. The author of this study is plan of further analysis by adding features and using cohort data.…”

Section: Introductionmentioning

confidence: 99%

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

Park,

Lee

2023

IEEE Access

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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.

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Child’s Target Height Prediction Evolution

Cited by 5 publications

References 34 publications

Adult height prediction using the growth curve comparison method

Adult height prediction using the growth curve comparison method

PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning

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

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