Department of Computer Application, Dinabandhu Andrews Institute of Technology and Management, Kolkata, India.
Department of Medical Lab Technology, Dinabandhu Andrews Institute of Technology and Management, Kolkata, India.
Epidemiol Infect. 2023 Aug 30;151:e159. doi: 10.1017/S0950268823001383.
Coronaviruses of the human variety have been the culprit of global epidemics of varying levels of lethality, including COVID-19, which has impacted more than 200 countries and resulted in 5.7 million fatalities as of May 2022. Effective clinical management necessitates the allocation of sufficient resources and the employment of appropriately skilled personnel. The elderly population and individuals with diabetes are at increased risk of more severe manifestations of COVID-19. Countries with a higher gross domestic product (GDP) typically exhibit superior health outcomes and reduced mortality rates. Here, we suggest a predictive model for the density of medical doctors and nursing personnel for 134 countries using a support vector machine (SVM). The model was trained in 107 countries and tested in 27, with promising results shown by the kappa statistics and ROC analysis. The SVM model used for predictions showed promising results with a high level of agreement between actual and predicted cluster values.
人类冠状病毒一直是各种致命程度的全球疫情的罪魁祸首，包括 COVID-19，截至 2022 年 5 月，它已经影响了 200 多个国家，导致 570 万人死亡。有效的临床管理需要分配足够的资源和使用适当技能的人员。老年人口和糖尿病患者患 COVID-19 更严重症状的风险增加。国内生产总值（GDP）较高的国家通常具有更好的健康结果和更低的死亡率。在这里，我们使用支持向量机（SVM）为 134 个国家提出了一个医生和护士人员密度的预测模型。该模型在 107 个国家进行了训练，在 27 个国家进行了测试，kappa 统计和 ROC 分析显示出了有希望的结果。用于预测的 SVM 模型表现出了良好的结果，实际和预测聚类值之间具有高度的一致性。
