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使用机器学习与脉冲振荡计数据来开发慢性阻塞性肺病和喘的预测模型
Chien-Hua Huang1, Kun-Ta Chou2,3, Diahn-Warng Perng2,3
1Department of Eldercare, College of Nursing, Central Taiwan University of Science and Technology, Taichung 406053, Taiwan.
Journal of personalized medicine
|April 27, 2024
概括
机器学习准确地使用脉冲振荡计系统 (IOS) 数据对喘和慢性肺炎进行分类. 这种方法有助于诊断和管理阻塞性肺部疾病.
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:
- 脉冲振荡计系统 (IOS) 为呼吸评估提供了有价值的数据.
- 精确区分喘和慢性阻塞性肺病 (COPD) 在临床上具有意义.
- 机器学习为分析复杂的生理数据集提供了潜力.
研究的目的:
- 开发和验证一种机器学习模型,用于将患者分为三个类别:没有气流阻塞,喘或COPD,使用IOS配置文件.
- 确定机器学习在区分阻塞性肺部疾病方面的有效性,仅基于IOS数据.
- 为了确定最佳的IOS参数和机器学习算法以进行准确的分类.
主要方法:
- 利用480名患者 (240名COPD患者,240名喘患者) 和84名健康个体的数据集进行模型培训.
- 将生理和IOS参数组合成六种特征组合.
- 评估了五种分类算法:逻辑回归,随机森林,神经网络,k-最近邻居和支持向量机.
主要成果:
- 神经网络分类器在将个人分类为没有阻塞,喘或COPD类别方面取得了最高的准确性 (0.786).
- 为区分健康和不健康个体的最佳分类涉及23个特征和神经网络 (准确率0.929).
- 在使用神经网络的15个特征中,区分COPD和喘产生了最好的结果 (准确率为0.854).
结论:
- 使用IOS配置文件的机器学习模型可以准确地分类阻塞性肺部疾病.
- 神经网络算法在区分健康个体,喘和COPD方面表现出卓越的表现.
- 基于IOS数据的模型显示,改善COPD和喘的管理是有希望的.
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