可解释的机器学习模型用于预测45-55岁的台湾男性非吸烟者的FEV1
Chih-Yueh Chang1,2,3, Dee Pei4, Yen-Liang Kuo1,2
1Division of Chest Medicine, Department of Internal Medicine, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 243089, Taiwan.
Diagnostics (Basel, Switzerland)
|December 30, 2025
概括
与传统回归相比,机器学习模型稍微提高了强迫呼气体积在一秒内 (FEV1) 的预测准确度. 影响FEV1的关键因素包括乳酸脱酶,体重和身体活动.
科学领域:
- 肺部医学 肺部医学
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 传统的回归模型无法充分解释一秒内强制呼气体积 (FEV1) 的变化.
- 机器学习 (ML) 提供了在FEV1预测中识别非线性模式的潜力.
- 分析了45-55岁的台湾男性非吸烟者.
研究的目的:
- 将ML模型 (随机森林,随机梯度提升,XGBoost) 的预测性能与FEV1.1的多重线性回归 (MLR) 进行比较.
- 使用ML识别FEV1的关键预测因子,并解释其影响.
- 评估ML在理解FEV1决定因素方面的实用性.
主要方法:
- 来自MJ健康查队列的45-55岁的23943名台湾男性非吸烟者的分析.
- 随机森林,随机梯度提升和XGBoost与使用重复列车测试分割的多重线性回归进行比较.
- 使用RMSE,RAE,RRSE和SMAPE评估模型性能;使用Shapley添加式解释 (SHAP) 解释变量重要性.
主要成果:
- ML模型显示的预测误差略低于MLR.
- FEV1的主要预测因素包括乳酸脱酶 (LDH),体重 (BW),教育水平,白细胞计数,总 bilirubin 和运动区域.
- SHAP分析显示,LDH和白细胞计数存在负相关性,而BW, bilirubin,教育和体力活动存在正相关性.
结论:
- 在FEV1预测中,ML方法比MLR提供了适度的准确性改进和增强的可解释性.
- 生物化学 (例如,LDH,胆红素) 和生活方式因素 (例如,BW,教育,体力活动,炎症标志物) 在健康的中年男性中对FEV1有显著的贡献.
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