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Rodrigo Yáñez-Sepúlveda¹, Rodrigo Olivares², Pablo Olivares²

¹Faculty Education and Social Sciences, Universidad Andres Bello, Viña del Mar 2520000, Chile.

Sports (Basel, Switzerland)

|August 27, 2025

概括

机器学习,特别是渐变增强,有效地使用体能测试对青少年的心脏代谢风险进行分类. 这种数据驱动的方法有助于早期发现和查年轻人.

科学领域:

公共卫生和预防医学
计算生物学和生物信息学
青少年健康与体育科学

背景情况:

青少年心脏代谢风险是一个重要的公共卫生问题.
身体健康是影响心脏代谢健康的关键可改变因素.
传统的统计方法在健身和人体统计数据中存在复杂的关系.

研究的目的:

开发和评估监督机器学习算法来分类青少年心脏代谢风险.
用标准化的身体健康评估作为风险预测模型的输入.
比较各种机器学习模型在识别有风险的青少年方面的表现.

主要方法:

一个代表性学龄青少年样本的横截面分析.
包括现场体能测试:心脏呼吸能力 (VO2max),肌肉力量 (俯仰) 和爆炸力 (水平跳跃).
使用精度,F1分数,回忆和AUC-ROC指标的监督机器学习模型 (例如人工神经网络,集合方法) 的应用和比较.

主要成果:

梯度提升分类器在测试模型中表现出卓越的性能.
获得了77. 0%的准确性,67. 3%的F1分数,以及最高的AUC-ROC (0. 601),表明有效的风险分类.
水平跳跃和俯卧运动被确定为心脏代谢风险的最重要的预测变量.

结论:

渐变增强是利用身体健康数据预测青少年心脏代谢风险的一个非常有效的模型.
这种机器学习方法为青少年早期风险检测提供了实用,数据驱动的工具.
这些发现支持在教育和临床环境中可扩展的查计划的潜力.

关键词:

年轻人增强梯度健康问题身体健康预测模型

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