通过使用可解释的机器学习,确定预测青春期前团队体育运动员在峰值高度速度时的年龄的关键因素
Nikolaos-Orestis Retzepis1, Alexandra Avloniti1, Christos Kokkotis1
1School of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece.
Sports (Basel, Switzerland)
|November 26, 2024
概括
了解儿童在团队体育中的成熟对于人才识别至关重要. 这项研究使用可解释的人工智能来识别预测年轻运动员成熟度的身高和体重等关键因素,从而改善了人才评估.
科学领域:
- 儿科 体育科学 儿童体育科学
- 生物机械分析 生物机械分析
- 数据科学在体育中的应用
背景情况:
- 运动员的成熟显著影响了体育参与和人才识别.
- 目前用于了解年轻运动员成熟度因素的模型缺乏实际应用.
- 早期识别发育轨迹对于优化训练和预防受伤至关重要.
研究的目的:
- 探索影响11岁团队运动运动员成熟度的因素.
- 应用可解释的人工智能 (XAI) 模型,以更好地理解成熟度预测器.
- 开发一个更准确的模型来预测年轻运动员的成熟.
主要方法:
- 使用了三种机器学习 (ML) 分类器,具有顺序前特征选择 (SFFS).
- 使用后勤回归 (LR) 用于到期的二进制分类.
- 应用了SHapley添加剂解释 (SHAP) 来进行变量重要性分析和独立的样本t测试以确认.
主要成果:
- 使用后勤回归实现了96.67%的准确性.
- 确定了七个关键因素来区分成熟度:坐高,父亲的身高,身体脂肪,体重,身高和左/右手握力.
- SHAP分析提供了对每个确定因素的贡献的关键见解.
结论:
- 可解释的AI模型为增强年轻运动员成熟预测提供了显著的潜力.
- 来自这些模型的个性化洞察力可以改变运动中的儿科生长分析.
- 改进的成熟预测可以导致儿童运动员更好的体育表现和发展结果.
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