机器学习模型的开发和验证,用于使用基于Kinect的混合现实练习对乳腺癌幸存者进行与癌症相关的肉症的分类
Byunggul Lim1,2,3,4, Wook Song1,2,3,5
1Health and Exercise Science Laboratory, Department of Physical Education, Seoul National University, Seoul, Republic of Korea.
Translational cancer research
|August 12, 2025
概括
使用基于Kinect的混合现实 (KMR) 设备移动数据的机器学习模型可以准确地分类与癌症相关的肉症. 右膝曲是关键预测因素,使得乳腺癌幸存者的非侵入性查成为可能.
科学领域:
- 生物医学工程 生物医学工程
- 康复科学 康复科学 康复科学
- 在瘤学瘤学.
背景情况:
- 在癌症幸存者中,由于对CT和DXA等成像工具的访问有限,萨科佩尼亚的诊断不足.
- 运动数据分析提供了一个可扩展的,切实可行的替代方法,用于对肉症进行分类.
- 这项研究探讨了机器学习 (ML) 用于使用关节角度数据检测与癌症相关的肉症.
研究的目的:
- 开发和验证ML模型,以使用基于Kinect的混合现实 (KMR) 设备数据来分类与癌症相关的麻症.
- 为了提高癌症幸存者的萨尔科佩尼亚的分类准确性.
- 为了识别与运动相关的萨尔科佩尼亚的关键预测因素.
主要方法:
- 包括77名乳腺癌幸存者 (I-III阶段,已完成治疗≥6个月前,没有转移/并发症).
- 通过骨肌肉指数 (SMI) 和手握强度 (HGS) 诊断的萨尔科佩尼亚.
- 在8周的炼干预期间,KMR设备收集了关节角度数据;ML模型 (SVM,KNN,RF,XGB) 被训练和验证.
主要成果:
- 在测试组中,XGBoost (XGB) 模型在测试组中实现了高性能:94.7%的精度,91.2%的回忆,95.8%的精度,93.4%的F1得分,96.2%的AUC.
- 功能重要性分析确定右膝曲是最有影响力的预测因素.
- 在运动组的38名参与者中,9人 (23.6%) 在干预后仍然被归类为肉类.
结论:
- 在KMR联合数据上训练的XGBoost模型显示了sarcopenia分类的出色表现.
- 右膝曲是识别肉症的关键运动特征.
- 对于癌症幸存者来说,KMR设备运动分析是一个有前途的非侵入性工具,用于早期发现肉症和个性化干预.
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