使用全球医疗保健和社会经济特征的COVID-19集群识别和支持矢量机分类器模型构建
Soumya Kanti Guha1, Sandip Sadhukhan1, Sougata Niyogi2
1Department of Computer Application, Dinabandhu Andrews Institute of Technology and Management, Kolkata, India.
Epidemiology and infection
|August 30, 2023
概括
这项研究使用支持矢量机器 (SVM) 在134个国家开发了医疗保健工作者密度的预测模型. 该模型准确地预测了医生和护士人员的分布,有助于全球卫生资源分配.
科学领域:
- 流行病学 流行病学
- 卫生经济学 卫生经济学
- 计算生物学 计算生物学
背景情况:
- 由冠状病毒引起的全球流行病,如COVID-19,突出了有效的临床管理和资源分配的关键需求.
- 弱势群体,包括老年人和糖尿病患者,在流行病期间面临更高的风险.
- 社会经济因素,如国内生产总值 (GDP),与健康结果和死亡率相关.
研究的目的:
- 开发和验证134个国家的医生和护理人员密度的预测模型.
- 用机器学习技术评估模型在预测医疗保健劳动力分布方面的准确性.
主要方法:
- 一个支持向量机 (SVM) 模型被用于预测.
- 该模型使用来自107个国家的数据进行训练,并使用来自27个国家的数据进行验证.
- 使用kappa统计数据和接收器操作特征 (ROC) 分析来评估模型性能.
主要成果:
- SVM模型显示了对医疗保健人员密度的有希望的预测准确性.
- 观察到医生和护士密度的实际和预测集群值之间存在很高的一致性.
- 卡帕统计和ROC分析表明该模型的可靠性.
结论:
- 开发的SVM模型提供了一个可靠的工具,用于预测全球医生和护理人员的密度.
- 准确预测医疗保健劳动力分布可以支持战略性资源配置并改善疫情防控.
- 这种预测能力对于解决健康差距和加强全球卫生安全至关重要.
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