针对伊朗人口的基于机器学习的螺旋计参考值:来自Shahedieh PERSIAN队列的横截面研究
Mohammad Sadegh Loeloe1, Reyhane Sefidkar1, Seyyed Mohammad Tabatabaei2
1Center for Healthcare Data Modeling, Department of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Frontiers in medicine
|March 27, 2025
概括
这项研究使用KNN回归确定了伊朗成年人的新螺旋计参考值,优于现有方程. 现在可以使用Excel计算器来准确评估伊朗成年人的肺功能.
科学领域:
- 肺部医学 肺部医学
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 螺旋测量对于评估肺功能至关重要.
- 现有的螺旋测量标准值可能不能准确地代表像伊朗人这样多样化的人口.
- 准确的参考值是可靠的诊断和呼吸道疾病的管理所需的.
研究的目的:
- 为了确定健康的伊朗成年人的精神测量标准值.
- 将KNN回归的准确性与MLR和LMS模型进行比较,以预测螺旋计参数.
- 根据已建立的GLI-高加索和伊朗方程来评估衍生值的性能.
主要方法:
- 分析了998名健康的伊朗成年人 (PERSIAN研究) 的螺旋计数据.
- 使用K-最接近邻居 (KNN) 回归来推导FEV1,FVC,FEV1/FVC和FEF25-75%的参考值.
- 使用5倍交叉验证和平均平方误差 (MSE) 验证模型性能,将KNN回归与多线性回归 (MLR) 和Lambda-Mu-Sigma (LMS) 模型进行比较.
主要成果:
- 与MLR和LMS模型相比,KNN回归在预测螺旋计参数方面表现出更高的准确性.
- 衍生的螺旋测量值显示MSE低于GLI-高加索和现有的伊朗方程,这表明对伊朗人口的预测更好.
- 具体来说,使用KNN回归,女性预测FVC的MSE为0.159,明显低于伊朗 (0.344) 和GLI-高加索 (0.397) 方程.
结论:
- 机器学习,特别是KNN回归,提供了一种灵活而准确的方法来确定特定人群的螺旋计参考值.
- 开发的螺旋计参考值是为伊朗成年人口量身定制的.
- 基于这些发现的Excel计算器可以帮助医疗保健专业人员精确评估伊朗成年人的肺功能.
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