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Related Concept Videos

Polygenic Traits01:18

Polygenic Traits

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Prediction of Adult Height by Machine Learning Technique.

Michael Shmoish1, Alina German2,3, Nurit Devir4

  • 1Bioinformatics Knowledge Unit, The Lokey Center, Technion-Israel Institute of Technology, Haifa, Israel.

The Journal of Clinical Endocrinology and Metabolism
|February 19, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts adult height (AH) using early growth data. Sex and height by age six are key predictors, showing potential for clinical application.

Keywords:
Artificial intelligencechild growthgrowth analysesheight predictionrandom tree

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Area of Science:

  • Pediatric endocrinology
  • Biostatistics
  • Machine learning applications

Background:

  • Adult height (AH) prediction is crucial in clinical practice.
  • Current methods rely on skeletal maturation assessments.
  • Machine learning (ML) offers advanced data analysis for precise predictions.

Purpose of the Study:

  • To develop and validate ML models for predicting AH using growth data up to age six.
  • To identify key growth parameters influencing AH prediction.

Main Methods:

  • Utilized longitudinal growth data from multiple cohorts (Gothenburg, Edinburgh).
  • Trained and compared various ML regressors, with Random Forest (RF) selected as the optimal model.
  • Validated the RF model on independent datasets.

Main Results:

  • The RF model achieved high prediction accuracy (r=0.87-0.88, R2=0.75-0.77) on validation cohorts.
  • Sex and height between 3.4-6.0 years were the most significant predictors.
  • Prediction errors showed a tendency to overpredict for shorter and underpredict for taller individuals.

Conclusions:

  • Successfully demonstrated validated ML for AH prediction using pre-age-six growth data.
  • The ML model generalizes well to different populations.
  • ML-driven AH prediction holds promise for clinical decision-making.