COVID-19从症状到预测:一个统计和机器学习的方法
Bahjat Fakieh1, Farrukh Saleem2
1Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
使用机器学习预测COVID-19患者年龄组显示出强烈的症状关联. 组合方法,特别是堆叠,大大提高了预测准确度,有助于公共卫生战略.
科学领域:
- 数据科学数据科学数据科学
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 随着COVID-19的爆发,人们越来越需要数据驱动的公共卫生战略.
- 分析患者数据对于了解疾病模式和为干预提供信息至关重要.
研究的目的:
- 使用统计和机器学习技术预测COVID-19患者年龄组.
- 为了确定患者症状和年龄人口统计数据之间的关联.
- 评估各种机器学习和预测组合方法的有效性.
主要方法:
- 利用了超过10,000个匿名的COVID-19患者记录的数据集.
- 应用统计测试 (ANOVA,t测试) 用于变量评估.
- 使用的机器学习模型:决策树,天真贝斯,KNN,梯度增强树,SVM,随机森林.
- 实施的组合方法:袋装,提升和堆叠.
- 执行严格的数据预处理以优化模型性能.
主要成果:
- 确定了COVID-19关键症状和患者年龄组之间的显著关联.
- 合并方法,特别是使用随机森林作为meta-learner的堆叠方法,大大提高了预测准确性 (0.7054).
- 堆叠增强了K-最近邻居 (0.529到0.63) 和天真贝叶斯 (0.554到0.622) 的性能.
结论:
- 机器学习,特别是集体堆叠,提供了一种强大的方法,可以根据症状预测COVID-19患者的年龄组.
- 调查结果可以为针对不同年龄人口的目标公共卫生战略,资源配置和治疗方案提供信息.
- 将预测模型集成到临床环境中,支持在流行病期间实时响应和干预.
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