ArticleSports (Basel, Switzerland)2024
Identifying Key Factors for Predicting the Age at Peak Height Velocity in Preadolescent Team Sports Athletes Using Explainable Machine Learning.
Article in Sports (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Role of Machine Learning in Talent Identification for Team Sports: A Systematic Review.Journal of sports science & medicine · 2026Pooled it
- Injury Prediction and Risk Modelling in Team Sports Using Artificial Intelligence and Sensor-Based Monitoring: A Scoping Review.Journal of functional morphology and kinesiology · 2026Review
- Gender-related differences in brace adherence among adolescents with idiopathic scoliosis.Spine deformity · 2026Article
- A Maturation-Aware Machine Learning Framework for Screening the Nutritional Status of Adolescents.Nutrients · 2026Article
- Interpretable Machine Learning for Osteopenia Detection: A Proof-of-Concept Study Using Bioelectrical Impedance in Perimenopausal Women.Journal of functional morphology and kinesiology · 2025Article
- The Effect of Peak Height Velocity on Strength and Power Development of Young Athletes: A Scoping Review.Journal of functional morphology and kinesiology · 2025Review
- Identification of Athleticism and Sports Profiles Throughout Machine Learning Applied to Heart Rate Variability.Sports (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Maturation is a key factor in sports participation and often determines the young athletes' characterization as a talent. However, there is no evidence of practical models for understanding the factors that discriminate children according to maturity. Hence, this study aims to deepen the understanding of the factors that affect maturity in 11-year-old Team Sports Athletes by utilizing explainable artificial intelligence (XAI) models. We utilized three established machine learning (ML) classifiers and applied the Sequential Forward Feature Selection (SFFS) algorithm to each. In this binary classification task, the logistic regression (LR) classifier achieved a top accuracy of 96.67% using the seven most informative factors (Sitting Height, Father's Height, Body Fat, Weight, Height, Left and Right-Hand Grip Strength). The SHapley Additive exPlanations (SHAP) model was instrumental in identifying the contribution of each factor, offering key insights into variable importance. Independent sample
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.