机器学习模型用于使用骨姿势和运动来分类非特异性部疼痛
Ui-Jae Hwang1, Oh-Yun Kwon2, Jun-Hee Kim1
1Department of Physical Therapy, College of Health Science, Laboratory of KEMA AI Research (KAIR), Yonsei University, Wonju, 26426, Republic of Korea.
Musculoskeletal science & practice
|March 25, 2024
概括
机器学习模型准确地对办公室工作者的非特异性部疼痛 (NSNP) 进行分类,使用运动期间的宫动力学. 这种方法在预测NSNP方面优于椎骨姿势评估.
科学领域:
- 生物力学和物理治疗.
- 医疗保健中的机器学习
- 职业健康 职业健康 职业健康
背景情况:
- 非特异性部疼痛 (NSNP) 在办公室工作者中很普遍.
- 椎姿势 (CCP) 和宫动力学是常用的查方法.
- 对于NSNP分类的机器学习 (ML) 模型的预测性能需要进一步研究.
研究的目的:
- 为了比较ML模型的预测性能,用于分类具有和没有NSNP的个体.
- 评估数据集,包括延伸和收缩 (CKdPR) 期间的CCP和宫动力学.
主要方法:
- 这是一项涉及773名公共服务办公室工作人员 (PSOW) 的探索性,横截面研究.
- 创建了五个数据集:CCP,延伸期间的宫动力学,收缩期间的宫动力学,CKdPR,以及CCP和CKdPR的组合.
- 四个ML算法 (随机森林,后勤回归,极端梯度提升,支持矢量机) 被训练并使用AUC,准确性,精度,回忆和F1得分进行评估.
主要成果:
- 使用CKdPR数据集的随机森林模型实现了最高的AUC (0.892) 和F1得分 (0.832) 来分类NSNP.
- 使用CCP数据集的随机森林模型显示出最低的性能 (AUC,0.738;F1,0.715).
结论:
- 与经典的统计方法相比,ML算法在分类NSNP方面表现优越.
- 在延伸和收缩 (CKdPR) 期间的宫动力学数据集为NSNP预测提供了比CCP数据集更好的ML模型性能.
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