论文标题
使用机器学习的冠状病毒状况分析和预测：孟加拉国人口的研究
Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population
论文作者
论文摘要
在大流行期间，患者感染率的早期预后可以通过确保治疗设施和适当的资源分配来减少死亡。近几个月来，与孟加拉国的死亡人数和感染率的数量相比有所增加。该国正在努力为许多患者提供适度的医疗。这项研究区分了机器学习模型，并创建了预测未来几天感染和死亡率的预测系统。从2020年3月1日到2021年8月10日，为数据集提供了数据集，对多层感知器（MLP）模型进行了培训。该数据是从值得信赖的政府网站管理的，并为培训目的手动编造。几个测试用例决定了模型的准确性和预测能力。特定模型之间的比较假设MLP模型比支持向量回归（SVR）和线性回归模型具有更可靠的预测能力。该模型介绍了有关危险情况和即将发生的冠状病毒病（COVID-19）攻击的报告。根据该模型产生的预测，孟加拉国可能会遭受另一次Covid-19攻击，在该攻击中，受感染案件的数量可以在929至2443之间，死亡病例在19至57之间。
During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days. Equipping a dataset with data from March 1, 2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The data was managed from a trusted government website and concocted manually for training purposes. Several test cases determine the model's accuracy and prediction capability. The comparison between specific models assumes that the MLP model has more reliable prediction capability than the support vector regression (SVR) and linear regression model. The model presents a report about the risky situation and impending coronavirus disease (COVID-19) attack. According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57.
