基于带有因果和信息域特征的心肺呼吸信号的儿童健康状况的机器学习分类应用-一项探索性研究
Maciej Rosoł1, Jakub S Gąsior2, Kacper Korzeniewski1
1Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland.
机器学习准确地使用心肺呼吸数据对儿科患者进行分类. 心血管连接特征显著提高了健康状况评估的分类准确性.
科学领域:
- 心脏病学 心脏病学
- 计算生物学 计算生物学
- 儿科 儿科 儿科
背景情况:
- 儿童心肺呼吸系统健康评估至关重要.
- 机器学习 (ML) 提供了客观分类的潜力.
- 心肺连接 (CRC) 功能可能会提高诊断准确性.
研究的目的:
- 在分类儿科组 (患者,健康,运动员) 中评估ML准确性.
- 评估CRC特征对分类性能的影响.
- 确定专业应用的最佳心肺功能集.
主要方法:
- 使用了六个数据集,具有不同的心肺呼吸系统参数.
- 应用了多个ML算法用于对象分类.
- 使用可解释的人工智能 (XAI) 进行特征重要性分析.
主要成果:
- 使用人口,心脏,呼吸道和CRC特征实现了超过89%的准确性.
- 第二个最佳准确度 (85%) 获得了影响力特征,不包括人口统计数据.
- CRC功能显著提高了分类准确性,这是XAI证实的.
结论:
- ML和综合性心肺呼吸系统特征有效地对儿科健康状况进行分类.
- ML和XAI在推进心肺呼吸信号分析方面表现有前途.
- 在培训/康复监测中潜在使用的最佳特征集.
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