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使用机器学习算法来预测COVID-19疫苗接种率:在加纳引入COVID-19疫苗一年后

Cornelius C Dodoo¹, Ebo Hanson-Yamoah¹, David Adedia²

¹School of Pharmacy, University of Health and Allied Sciences, Ho, Ghana.

|March 6, 2024

概括

了解加纳对COVID-19疫苗的犹至关重要. 机器学习确定了设施类型和潜在条件作为疫苗接种的关键预测因素,指导了有针对性的干预措施.

科学领域:

公共卫生公共卫生
流行病学流行病学
医疗信息学医疗信息学

背景情况:

疫苗犹带来了重大的全球健康风险,正如COVID-19大流行期间所强调的那样.
了解COVID-19疫苗接种障碍对于制定有效的公共卫生战略至关重要.

研究的目的:

确定影响加纳COVID-19疫苗接种的因素和障碍.
利用机器学习算法来预测疫苗的接受度.

主要方法:

通过使用自我管理的问卷进行了一项横截面研究.
机器学习模型,包括随机森林模型,用于分析疫苗接种的预测因素.
数据来自加纳一家三级和四级医院的客户.

主要成果:

机器学习模型有效预测了COVID-19疫苗接种率.
参观的医疗机构类型和潜在疾病的存在是重要的预测因素.
该研究证明了机器学习在理解疫苗吸收模式方面的有用性.

结论:

机器学习算法可以准确预测COVID-19疫苗的接种率.
识别关键预测因素可以开发有针对性的干预措施,以增加疫苗的接受度.
医疗机构类型和患者健康状况是影响疫苗接种决定的关键因素.

关键词:

这是COVID-19疫苗.机器学习是机器学习.疫苗的犹疫苗的犹

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