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首页> 外文期刊>Orthopaedic Journal of Sports Medicine >Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017
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Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017

机译：机器学习优于逻辑回归分析，以预测下一个季节NHL球员伤害：从2007年到2017年的2322名球员分析

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Background: The opportunity to quantitatively predict next-season injury risk in the National Hockey League (NHL) has become a reality with the advent of advanced computational processors and machine learning (ML) architecture. Unlike static regression analyses that provide a momentary prediction, ML algorithms are dynamic in that they are readily capable of imbibing historical data to build a framework that improves with additive data. Purpose: To (1) characterize the epidemiology of publicly reported NHL injuries from 2007 to 2017, (2) determine the validity of a machine learning model in predicting next-season injury risk for both goalies and position players, and (3) compare the performance of modern ML algorithms versus logistic regression (LR) analyses. Study Design: Descriptive epidemiology study. Methods: Professional NHL player data were compiled for the years 2007 to 2017 from 2 publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included age, 85 performance metrics, and injury history. A total of 5 ML algorithms were created for both position player and goalie data: random forest, K Nearest Neighbors, Na?ve Bayes, XGBoost, and Top 3 Ensemble. LR was also performed for both position player and goalie data. Area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 2109 position players and 213 goalies. For models predicting next-season injury risk for position players, XGBoost performed the best with an AUC of 0.948, compared with an AUC of 0.937 for LR ( P & .0001). For models predicting next-season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared with an AUC of 0.947 for LR ( P & .0001). Conclusion: Advanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season.
机译：背景：在国家曲棍球联赛（NHL）中定量预测下一个季节伤害风险的机会已成为一个现实，已成为先进的计算处理器和机器学习（ML）架构的出现。与提供瞬间预测的静态回归分析不同，ML算法是动态的，因为它们很容易吸收历史数据以构建与添加剂数据有所改善的框架。目的：至（1）表征2007年至2017年公开报告的NHL损伤的流行病学，（2）确定机器学习模型在预测守门员和位置球员的下一个季节伤害风险方面的有效性，（3）比较现代ML算法的性能与逻辑回归（LR）分析。研究设计：描述性流行病学研究。方法：在缺乏官方NHL批准的数据库中，从2个公开报告的数据库中编制了专业的NHL播放器数据2007年至2017年。从每个专业年份从每个专业年份获取的属性包括年龄，85个绩效指标和伤害历史。为两个位置玩家和守门员数据创建了总共5毫升算法：随机森林，K最近邻居，Na？ve Bayes，XGBoost和前三名合奏。 LR也用于播放器和守门员数据。接收器下的区域，操作特征曲线（AUC）主要确定验证。结果：玩家数据是从2109个位置玩家和213个守门员产生的。对于预测位置播放器的下一个季节伤害风险的模型，XGBoost与0.948的AUC相比，对于LR的AUC（P <.0001）相比，XGBoost通过0.948的AUC进行了最佳状态。对于预测守门员的下一个季节伤害风险的模型，XGBoost具有0.956的最高AUC，而LR的AUC为0.947（P <.0001）。结论：XGBoost等先进的ML型号，表现出良好的良好能力，可以预测下一个季节是否可能发生公开可报告的伤害。

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