基于成人基本健康检查数据的Helicobacter pylori感染预测的机器学习:一项回顾性研究
Qiaoli Wang1, Tao Liang2, Yuexi Li1
1Health Management Center, Deyang People's Hospital, Deyang, Sichuan, China.
Frontiers in medicine
|June 30, 2025
概括
机器学习模型可以使用健康查数据来预测Helicobacter pylori (H. pylori) 感染的非侵入性方法. 额外树木分类器表现最好,提供了潜在的临床决策支持工具.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 流行病学 流行病学
背景情况:
- *Helicobacter pylori (H. pylori) 感染是一个重大的全球健康问题.
- * 非侵入性诊断方法对于早期检测和管理至关重要.
- *现有的诊断工具可能在可访问性或成本方面存在局限性.
研究的目的:
- *评估开发机器学习 (ML) 模型的可行性,以进行非侵入性H.pylori感染预测.
- *利用常规收集的成年人健康查数据,包括人口统计和生物标志物.
- *为临床实践建立一个潜在的决策支持工具.
主要方法:
- *数据来源于成年人的健康检查记录.
- * 最小绝对收缩和选择运算符 (LASSO) 回归用于特征选择.
- *使用了6个ML算法 (包括额外树,随机森林,XGBoost).
- *夏普利添加式解释 (SHAP) 用于模型的可解释性.
主要成果:
- *包括10,393名受试者;31.54%的人患有H pylori感染.
- * 额外树木模型实现了最高的AUC (0.827),准确性 (0.744),和回忆 (0.736).
- *SHAP分析确定年龄,白血球,白血球,性别和腰围为关键预测因素.
结论:
- * 额外树木分类器证明了H pylori感染预测的最佳性能.
- * SHAP分析提供了对模型解释性和关键风险因素的宝贵见解.
- * 开发的模型为早期临床预测和干预策略提供了潜力.
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