基于BMI和肺透气参数的正常,超重和肥胖个体中,用于检测与扩散异常相关的呼吸变化的机器学习:一项观察性研究
Xin-Yue Song1, Xin-Peng Xie2, Wen-Jing Xu1
1Department of Respiratory and Critical Care Medicine, West China School of Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
机器学习有效地使用BMI和肺功能检测扩散异常. 随机森林模型准确地识别了扩散异常,有助于在重量级别的诊断.
科学领域:
- 肺部医学 肺部医学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 机器学习 (ML) 算法可以检测与扩散异常相关的呼吸系统变化.
- 这些算法利用身体质量指数 (BMI) 和肺透气参数.
- 对监督的ML算法的有效性进行了评估,以确定最佳配置.
研究的目的:
- 评估监督的ML算法在检测扩散异常方面的有效性.
- 根据BMI和肺透气参数,确定最佳的ML配置来诊断扩散异常.
- 分析BMI与一氧化碳 (DLCO) 扩散能力之间的关系.
主要方法:
- 追溯分析了440个人的肺功能测试数据 (正常扩散能力[DN]与扩散异常[DA]).
- 统计比较 (t-test,Chi-square) 和分片回归来分析BMI和DLCO之间的相关性.
- 应用监督的ML算法 (SVM,RF,AdaBoost,BAYES,KNN) 和特征选择 (SelectKBest,RFECV,SelectFromModel) 的应用.
- 使用SHAP进行特征重要性分析和换重要性.
主要成果:
- 与DN相比,DA患者的峰值呼气流 (PEF) 和DLCO较低.
- 体重指数显示,BMI 18.540 kg/m2与DLCO的立方关系,BMI ≥40 kg/m2的负线性相关性.
- 随机森林 (RF) 获得了最高的诊断准确度 (AUC=0.983) 来区分DN和DA.
- 确定的最佳参数是BMI,FEV1/FVC和VC,与特征重要性分析保持一致.
结论:
- 扩散能力随着BMI升高至40kg/m2而提高,然后随着BMI升高而下降.
- 将BMI和肺通风参数与ML算法相结合是诊断扩散异常的实用方法.
- 这种方法适用于正常体重,超重和肥胖的个体,特别是使用便携式螺旋计.
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