使用机器学习与随机森林和XGBoost构建用于诊断成人喘的诊断算法
Katsuyuki Tomita1, Akira Yamasaki2, Ryohei Katou1
1Department of Respiratory Medicine, Yonago Medical Center, National Hospital Organization, Yonago 683-0006, Japan.
Diagnostics (Basel, Switzerland)
|October 14, 2023
概括
这项研究使用机器学习分类器开发了一种人工智能驱动的成人喘诊断工具. 极端梯度提升模型实现了81%的准确性,有助于有效的喘诊断和管理.
科学领域:
- 医疗信息学 医疗信息学
- 肺部病理学 肺部病理学
- 医疗保健中的机器学习
背景情况:
- 有效的成人喘诊断需要基于证据的算法.
- 非特异性呼吸道症状在门诊环境中很常见.
- 机器学习为诊断支持提供了潜力.
研究的目的:
- 开发和评估基于机器学习的成人喘诊断算法.
- 为了比较随机森林 (RF) 和极端梯度增强 (XGBoost) 分类器的性能.
- 为了确定喘诊断的关键特征.
主要方法:
- 利用了566名有呼吸道症状的成年门诊患者的医疗记录.
- 采用随机森林 (RF) 和优化的极端梯度提升 (XGBoost) 分类器.
- 使用贝叶斯优化优化超参数,并使用精度和AUC进行评估.
主要成果:
- XGBoost 分类器实现了81%的准确性和85%的AUC,超过了射频分类器.
- 使用症状,身体征兆和肺功能测试的组合构建了一个诊断算法.
- 功能重要性分析确定了喘诊断的关键指标.
结论:
- 拟议的XGBoost模型可以作为诊断成人喘的可靠辅助工具.
- 该模型有效地整合了临床数据和客观测试结果.
- 这种方法可以适应在各种临床环境中构建诊断算法.
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