老年人中COVID-19风险分层:一种机器学习方法来识别个人和健康相关的风险因素
Arezoo Abasi1, Seyed Abbas Motevalian2,3, Haleh Ayatollahi4
1Student Research Committee, Iran University of Medical Sciences, Tehran, Iran.
BMC public health
|July 29, 2025
概括
这项研究开发了一种预测模型来分类COVID-19风险,识别知识和健康问题等关键因素. 机器学习准确地对个人进行了分类,为公共卫生战略提供了信息.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 随着COVID-19的爆发,人们越来越需要了解影响风险感知和健康行为的因素.
- 评估COVID-19的个人风险对于有效的公共卫生干预至关重要.
研究的目的:
- 开发一个预测模型来分类COVID-19风险类别.
- 探索知识,感知和健康相关问题的作用,以确定COVID-19风险.
- 为风险评估整合聚类和模型解释技术.
主要方法:
- 聚类分析用于人口,健康和行为数据,以识别不同的风险组.
- 机器学习模型 (CatBoost,XGBoost,随机森林,GLM,决策树,H2O DNN,L2 SVM) 用于风险预测.
- 用SHAP (夏普利添加式扩展) 分析来确定模型的可解释性.
主要成果:
- 根据知识,健康因素和家庭诊断,确定了三个不同的COVID-19风险类别.
- 在风险分类中,L2 SVM显示了最高的准确性 (0.9724) ,其次是XGBoost和CatBoost.
- 通过SHAP分析,家庭卫生,高血压和胃肠道症状被确定为风险分类的重要驱动因素.
结论:
- 整合知识,感知和健康问题可以提高COVID-19风险评估的准确性.
- 公共卫生政策应应对身体和心理因素,以有效缓解.
- 数据驱动型号可以指导资源分配,并改善弱势群体的公共卫生响应.
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