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慢性阻塞性肺病 (COPD) 风险预测模型的构建和验证:基于2009年至2018年的NHANES数据库的横截面研究
Liqin Wang1,2, Shijia Zhang1,2, Zhaohong Gao1,2
1Heilongjiang University of Chinese Medicine, 24 Heping Road, Xiangfang District, Harbin, 150040, Heilongjiang, China.
BMC pulmonary medicine
|July 3, 2025
概括
这项研究开发了一种多层感知器 (MLP) 模型,用于预测慢性阻塞性肺病 (COPD) 风险,其性能优于其他机器学习模型. 该MLP模型提供了增强的解释性,识别了早期COPD查的关键风险因素.
科学领域:
- 计算生物学和生物信息学
- 公共卫生和流行病学.
- 机器学习在医疗保健中的应用
背景情况:
- 慢性阻塞性肺病 (COPD) 是一个重大的全球健康挑战.
- 早期识别高风险人群对于减轻COPD的影响至关重要.
- 目前用于COPD风险预测的机器学习模型往往缺乏特征维度和可解释性.
研究的目的:
- 整合国家健康和营养检查调查 (NHANES) 的多维特征,用于COPD风险预测.
- 为了比较物流回归,XGBoost和多层感知器 (MLP) 模型的性能.
- 通过可解释性分析,确定最佳预测模型并提高其临床适用性.
主要方法:
- 使用了2009-2018年的NHANES数据,并进行了系统的特征选择和预处理.
- 开发和评估了多变量二元逻辑回归,XGBoost和MLP模型.
- 使用分层五倍交叉验证和SHapley添加式解释 (SHAP) 进行模型解释.
主要成果:
- 该MLP模型实现了最高的性能 (精度:0.937,F1得分:0.657) 与最小的过.
- SHAP分析确定了关键预测因素:吸烟持续时间,喘,年龄,饮食状况,总蛋白质,RDW,BMI,婚姻状况,二手烟和总 bilirubin.
- 依赖图显示了连续变量的关键风险转折点.
结论:
- 使用大规模的多维数据开发了一种高性能和可解释的COPD风险预测模型 (MLP).
- 该MLP模型显示了有效识别COPD高风险个体的潜力.
- 未来的研究应该纳入纵向数据和多式联络信息,以完善预测准确性和早期查和个性化干预的临床实用性.
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