Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model  

作　　者：Vishan Kumar Gupta Avdhesh Gupta Dinesh Kumar Anjali Sardana 

机构地区：[1]Department of Computer Science and Engineering(CSE),Graphic Era Deemed to be University,Dehradun 248002,India [2]Department of CSE,IMS Engineering College,Ghaziabad 201009,India [3]Department of CSE,KIET Group of Institutions,Ghaziabad 201206,India

出　　处：《Big Data Mining and Analytics》2021年第2期116-123,共8页大数据挖掘与分析（英文）

摘　　要：A novel coronavirus(SARS-CoV-2) is an unusual viral pneumonia in patients, first found in late December 2019, latter it declared a pandemic by World Health Organizations because of its fatal effects on public health. In this present, cases of COVID-19 pandemic are exponentially increasing day by day in the whole world. Here, we are detecting the COVID-19 cases, i.e., confirmed, death, and cured cases in India only. We are performing this analysis based on the cases occurring in different states of India in chronological dates. Our dataset contains multiple classes so we are performing multi-class classification. On this dataset, first, we performed data cleansing and feature selection, then performed forecasting of all classes using random forest, linear model, support vector machine,decision tree, and neural network, where random forest model outperformed the others, therefore, the random forest is used for prediction and analysis of all the results. The K-fold cross-validation is performed to measure the consistency of the model.

关 键 词：CORONAVIRUS COVID-19 respiratory tract multi-class classification random forest 

分 类 号：R563.1[医药卫生—呼吸系统] TP181[医药卫生—内科学]