目的:腕管综合征(CTS)是最常见的上肢卡压神经病,具有多方面的病因,包括各种危险因素。这项研究的目的是调查人体测量的手,握力,和捏合强度可以通过机器学习技术作为CTS的预测指标。
方法:纳入包括表现出CTS症状的患者(n=56)和无症状的健康对照(n=56),通过电生理评估进行确认。使用数字卡尺获得手的人体测量,握力是通过数字手握测力计测量的,和捏合强度使用扣压计进行评估。采用四种最常见和最有效的机器学习算法进行了全面分析,整合彻底的参数调整和交叉验证程序。此外,给出了变量重要性的结果。
结果:在各种算法中,随机森林(准确率为89.474%,F1分数为0.905,卡伯值为0.789)和XGBoost(准确率为86.842%,基于不同的分类指标,F1评分为0.878,kappa值为0.736)成为表现最好的选择。此外,使用特定于这些模型的变量重要性计算,发现最重要的变量是腕围,手的宽度,手握力,尖端捏,钥匙捏,中指长度。
结论:这项研究的结果表明,腕围,手的宽度,手握力,尖端捏,钥匙捏,和中指长度可用作CTS的可靠指标。此外,这里开发的模型,连同确定的关键变量,可以作为医疗保健专业人员的信息指南,提高CTS预测的精度和有效性。
OBJECTIVE: Carpal tunnel syndrome (CTS) stands as the most prevalent upper extremity entrapment neuropathy, with a multifaceted etiology encompassing various risk factors. This study aimed to investigate whether anthropometric measurements of the hand, grip strength, and pinch strength could serve as predictive indicators for CTS through machine learning techniques.
METHODS: Enrollment encompassed patients exhibiting CTS symptoms (n = 56) and asymptomatic healthy controls (n = 56), with confirmation via electrophysiological assessments. Anthropometric measurements of the hand were obtained using a digital caliper, grip strength was gauged via a digital handgrip dynamometer, and pinch strengths were assessed using a pinchmeter. A comprehensive analysis was conducted employing four most common and effective machine learning algorithms, integrating thorough parameter tuning and cross-validation procedures. Additionally, the outcomes of variable importance were presented.
RESULTS: Among the diverse algorithms, Random Forests (accuracy of 89.474%, F1-score of 0.905, and kappa value of 0.789) and XGBoost (accuracy of 86.842%, F1-score of 0.878, and kappa value of 0.736) emerged as the top-performing choices based on distinct classification metrics. In addition, using variable importance calculations specific to these models, the most important variables were found to be wrist circumference, hand width, hand grip strength, tip pinch, key pinch, and middle finger length.
CONCLUSIONS: The findings of this study demonstrated that wrist circumference, hand width, hand grip strength, tip pinch, key pinch, and middle finger length can be utilized as reliable indicators of CTS. Also, the model developed herein, along with the identified crucial variables, could serve as an informative guide for healthcare professionals, enhancing precision and efficacy in CTS prediction.