Huang Yuanqi, Huang Shengqi, Wang Yukun, Li Yurong, Gui Yuheng, Huang Caihua
Research and Communication Center for Exercise and Health, Xiamen University of Technology, Xiamen, China.
School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, China.
Front Physiol. 2022 Sep 15;13:937546. doi: 10.3389/fphys.2022.937546. eCollection 2022.
The application of machine learning algorithms in studying injury assessment methods based on data analysis has recently provided a new research insight for sports injury prevention. However, the data used in these studies are primarily multi-source and multimodal (i.e., longitudinal repeated-measures data and cross-sectional data), resulting in the models not fully utilising the information in the data to reveal specific injury risk patterns. Therefore, this study proposed an injury risk prediction model based on a multi-modal strategy and machine learning algorithms to handle multi-source data better and predict injury risk. This study retrospectively analysed the routine monitoring data of sixteen young female basketball players. These data included training load, perceived well-being status, physiological response, physical performance and lower extremity non-contact injury registration. This study partitions the original dataset based on the frequency of data collection. Extreme gradient boosting (XGBoost) was used to construct unimodal submodels to obtain decision scores for each category of indicators. Ultimately, the decision scores from each submodel were fused using the random forest (RF) to generate a lower extremity non-contact injury risk prediction model at the decision-level. The 10-fold cross-validation results showed that the fusion model was effective in classifying non-injured (mean Precision: 0.9932, mean Recall: 0.9976, mean F2-score: 0.9967), minimal lower extremity non-contact injuries risk (mean Precision: 0.9317, mean Recall: 0.9167, mean F2-score: 0.9171), and mild lower extremity non-contact injuries risk (mean Precision: 0.9000, mean Recall: 0.9000, mean F2-score: 0.9000). The model performed significantly more optimal than the submodel. Comparing the fusion model proposed with a traditional data integration scheme, the average Precision and Recall improved by 8.2 and 20.3%, respectively. The decision curves analysis showed that the proposed fusion model provided a higher net benefit to athletes with potential lower extremity non-contact injury risk. The validity, feasibility and practicality of the proposed model have been confirmed. In addition, the shapley additive explanation (SHAP) and network visualisation revealed differences in lower extremity non-contact injury risk patterns across severity levels. The model proposed in this study provided a fresh perspective on injury prevention in future research.
机器学习算法在基于数据分析的损伤评估方法研究中的应用，最近为运动损伤预防提供了新的研究视角。然而，这些研究中使用的数据主要是多源和多模态的（即纵向重复测量数据和横断面数据），导致模型无法充分利用数据中的信息来揭示特定的损伤风险模式。因此，本研究提出了一种基于多模态策略和机器学习算法的损伤风险预测模型，以更好地处理多源数据并预测损伤风险。本研究回顾性分析了16名年轻女性篮球运动员的常规监测数据。这些数据包括训练负荷、主观幸福感状态、生理反应、身体表现以及下肢非接触性损伤记录。本研究根据数据收集频率对原始数据集进行划分。采用极端梯度提升（XGBoost）构建单模态子模型，以获得各类指标的决策分数。最终，使用随机森林（RF）融合每个子模型的决策分数，在决策层面生成下肢非接触性损伤风险预测模型。10折交叉验证结果表明，融合模型在对未受伤（平均精确率：0.9932，平均召回率：0.9976，平均F2分数：0.9967）、下肢非接触性损伤最小风险（平均精确率：0.9317，平均召回率：0.9167，平均F2分数：0.9171）和下肢非接触性损伤轻度风险（平均精确率：0.9000，平均召回率：0.9000，平均F2分数：0.9000）进行分类方面是有效的。该模型的表现明显优于子模型。与传统数据集成方案相比，所提出的融合模型的平均精确率和召回率分别提高了8.2%和20.3%。决策曲线分析表明，所提出的融合模型为有潜在下肢非接触性损伤风险的运动员提供了更高的净效益。所提模型的有效性、可行性和实用性得到了证实。此外，夏普利值附加解释（SHAP）和网络可视化揭示了不同严重程度下肢非接触性损伤风险模式的差异。本研究提出的模型为未来研究中的损伤预防提供了新的视角。
