目的 探讨通过构建极端梯度提升(XGBoost)机器学习算法模型,分析新兵下肢训练伤的发生情况及各危险因素预测效能。方法 采用整群抽样方法,选取武警某部参与2024年秋季新训的新兵279名为研究对象。通过问卷调查对个人情况、入伍前体能成绩及下肢训练伤的发生情况进行统计,根据秋训期间有无发生下肢训练伤分为下肢训练伤组和无下肢训练伤组,采用单因素分析比较两组个人情况、体能成绩等信息,使用最小绝对收缩和选择算子(LASSO)联合特征筛选出有统计学意义的变量后构建XGBoost机器学习算法的预测模型,结合沙普利加性解释(SHAP)特征重要性分析,量化各个特征变量对下肢训练伤的因素权重,并采用受试者操作特征(ROC)曲线检验模型的预测性能。结果 本研究共发放279份问卷,回收有效问卷256份,有效率91.76%。256名中发生下肢训练伤65名(25.39%)为下肢训练伤组,其余191名为无下肢训练伤组。单因素分析及LASSO回归共筛选出11个因素,包括BMI、兵源地、饮酒史、入伍前伤病史、参加役前训练、训练伤预防知识、训练前充分热身、训练后充分放松、3000 m成绩、仰卧起坐成绩、匹兹堡睡眠质量指数量表(PSQI)。以此构建的XGBoost模型曲线下面积为0.995(95% CI 0.989~1.000),预测性能良好。SHAP解释变量在模型中的重要性排序由高到低依次为PSQI量表、BMI、训练伤预防知识、仰卧起坐成绩、兵源地、训练后充分放松、3000 m成绩、参加役前训练、训练前充分热身、饮酒史、入伍前伤病史。结论 基于XGBoost算法构建的新兵下肢训练伤预测模型具有较好的预测效能,该模型结合SHAP技术,能量化危险因素对训练伤发生的相对权重,应在训练过程中优先控制高风险因素,从而降低伤害发生的概率。

Objective To explore the occurrence of lower limb training injuries in new recruits and the predictive efficacy of various risk factors by constructing an extreme gradient boosting (XGBoost) machine learning algorithm model. Methods Through cluster sampling, 279 new recruits participating in the 2024 autumn training of a certain military unit were selected as the research subjects. Personal information, physical fitness scores before enlistment, and the occurrence of lower limb training injuries of the recruits were statistically analyzed through questionnaires. The recruits were divided into a lower limb training injury group and a non-lower limb training injury group based on whether they had lower limb training injuries during the autumn training period. Univariate analysis was used to compare the personal information, physical fitness scores, and other information between the two groups. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to jointly screen statistically significant variables and a prediction model based on the XGBoost machine learning algorithm was constructed. Combined with the Shapley Additive Explanation (SHAP) feature importance analysis, the weight of each feature variable on the occurrence of lower limb training injuries was quantified, and the ROC was used to test the predictive performance of the model. Results A total of 279 questionnaires were distributed in the study, and 256 valid questionnaires were retrieved, with a recovery rate of 91.76%. Among the 256 recruits, 65 cases (25.39%) suffered from lower limb training injuries. Univariate analysis and LASSO regression identified 11 factors, including BMI, place of origin, drinking history, pre-enlistment injury history, participation in pre-service training, knowledge of training injury prevention, adequate warm-up before training, adequate relaxation after training, 3000m performance, sit-up performance, and Pittsburgh Sleep Quality Index (PSQI). The XGBoost model had an area under the curve of 0.995 (95% confidence interval: 0.989-1.000), indicating good predictive performance. The importance ranking of the explanatory variables in the mode from the highest to the lowest by SHAP values was PSQI scale, BMI, knowledge of training injury prevention, sit-up performance, place of origin, adequate relaxation after training, 3000m performance, participation in pre-service training, warm-up before training, drinking history, and pre-enlistment injury history. Conclusions The predictive model for lower limb training injuries in recruits, constructed based on the XGBoost algorithm, exhibits good predictive efficacy. This model, combined with the SHAP technique, can quantify the relative weights of risk factors on the occurrence of training injuries, and priority should be given to controlling high-risk factors during training to reduce the likelihood of injuries.