New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes
New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes
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DOI：
10.1055/a-1231-5304
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发表时间：
2020-09-13
影响因子：
2.5
通讯作者：
Ayramo, Sami
中科院分区：
文献类型：
--
作者：
Jauhiainen, Susanne;Kauppi, Jukka-Pekka;Ayramo, Sami
本文的目的是介绍如何利用预测机器学习方法以数据驱动的方式检测运动损伤风险因素。该方法可用于寻找风险因素的新假设并确认先前已识别的假设的预测能力。我们使用了 314 名年轻篮球和福乐球运动员（48.4% 男性，15.72 +/- 1.79 岁，173.34 +/- 9.14 厘米，64.65 +/- 10.4 公斤）的三维运动分析和身体数据。在三年随访期间，使用线性（L1 正则逻辑回归）和非线性方法（随机森林）来预测中度和重度膝关节和踝关节损伤 (N=57)。结果通过排列检验得到证实，并通过 Wilcoxon 符号秩检验检测到预测风险因素 (p
The purpose of this article is to present how predictive machine learning methods can be utilized for detecting sport injury risk factors in a data-driven manner. The approach can be used for finding new hypotheses for risk factors and confirming the predictive power of previously recognized ones. We used three-dimensional motion analysis and physical data from 314 young basketball and floorball players (48.4% males, 15.72 +/- 1.79 yr, 173.34 +/- 9.14cm, 64.65 +/- 10.4kg). Both linear (L1-regularized logistic regression) and non-linear methods (random forest) were used to predict moderate and severe knee and ankle injuries (N=57) during three-year follow-up. Results were confirmed with permutation tests and predictive risk factors detected with Wilcoxon signed-rank-test (p