神经网络方法研究测试项对预测慢性阻塞性肺部疾病中的身体活动的重要性
Yoshiki Nakahara1, Shingo Mabu1, Tsunahiko Hirano2
1Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi 7558611, Japan.
Journal of clinical medicine
|July 14, 2023
概括
机器学习使用常规测试准确预测COPD患者的体力活动减少. 通过链式方程 (MICE) 进行多重归算改善了预测,识别了性别,吸烟史和查营养状况等关键因素.
科学领域:
- 肺部医学 肺部医学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 慢性阻塞性肺病 (COPD) 严重影响身体活动和日常功能.
- 这种体力活动障碍的根本原因尚不清楚,目前的评估方法需要专门的,昂贵的设备.
- 对于评估COPD患者体力活动限制的准确和可访问的方法是常规临床实践所需的.
研究的目的:
- 开发和验证用于预测COPD患者身体活动水平的机器学习模型.
- 确定影响COPD体育活动减少的关键临床和人口因素.
- 评估不同数据预处理技术对预测身体活动的有效性.
主要方法:
- 利用了来自COPD患者的常规临床测试项目的数据集.
- 应用了三种数据预处理方法:零填充,通过链式方程 (MICE) 进行多重归算,以及k-最近邻居 (kNN).
- 构建神经网络来预测身体活动,并使用变的重要性来识别重要的预测因素.
主要成果:
- 通过链式方程 (MICE) 的多重推算产生了最高的预测准确度 (73.00%),超过了零填充 (68.44%),kNN (71.52%),和XGBoost (66.12%).
- 该模型在识别严重减少体力活动的患者时实现了高灵敏度 (89.36%).
- 发现的关键预测因素包括性别,吸烟史,年龄和全身相角 (营养状况).
结论:
- 机器学习,特别是MICE预处理,提供了一种可行的方法,可以使用标准临床数据预测COPD患者的体力活动障碍.
- 常规的临床数据可用于有效查体育活动限制,减少对专门设备的需求.
- 性别,吸烟史,年龄和营养状况是评估COPD体育活动减少的关键指标.
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