ArticlePhenomics (Cham, Switzerland)2024
Improved prediction of swimming talent through random forest analysis of anthropometric and physiological phenotypes.
Article in Phenomics (Cham, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Prediction of early treatment response to drug-eluting beads chemoembolization for hepatocellular carcinoma using machine learning.Frontiers in pharmacology · 2026Article
- Determinants of 50 m Front Crawl Performance in Adolescent Non-Elite Female Swimmers: A Longitudinal Study.Journal of functional morphology and kinesiology · 2025Article
- Higher relevance of mechanical determinants for short-distance performance and metabolic determinants for middle-distance performance in female adolescent swimmers at national level.Scientific reports · 2025Article
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Authors and funding
9 authors.
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Abstract
The field of competitive swimming lacks broadly applicable predictive models for talent identification across various age groups of adolescent swimmers. This study aimed to construct a predictive model for athletic talent using machine learning methods based on anthropometric and physiological data. Baseline data were collected from 5444 participants aged 10-18 in Shanghai, China, between 2015 and 2018, with 4969 completing a 3-year follow-up. Talents were discerned based on their performance over the follow-up period, revealing age- and sex- dependent developmental differences between swimmers classified as talented versus non-talented. After controlling for confounding variables, age and sex, nine machine learning algorithms were employed, with Random Forest achieving the highest performance and being selected as the final model. The model demonstrated excellent predictive performance on both the test dataset and an independent validation dataset from Shandong ( Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-024-00176-8.
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