Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model
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 da...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2021-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020016 |
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| author | Vishan Kumar Gupta Avdhesh Gupta Dinesh Kumar Anjali Sardana |
| author_facet | Vishan Kumar Gupta Avdhesh Gupta Dinesh Kumar Anjali Sardana |
| author_sort | Vishan Kumar Gupta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2f95f5541c214de5811d42eb26f9699b |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-2f95f5541c214de5811d42eb26f9699b2025-02-02T03:45:09ZengTsinghua University PressBig Data Mining and Analytics2096-06542021-06-014211612310.26599/BDMA.2020.9020016Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest ModelVishan Kumar Gupta0Avdhesh Gupta1Dinesh Kumar2Anjali Sardana3<institution content-type="dept">Department of Computer Science and Engineering (CSE)</institution>, <institution>Graphic Era Deemed to be University</institution>, <city>Dehradun</city> <postal-code>248002</postal-code>, <country>India</country><institution content-type="dept">Department of CSE</institution>, <institution>IMS Engineering College</institution>, <city>Ghaziabad</city> <postal-code>201009</postal-code>, <country>India</country><institution content-type="dept">Department of CSE</institution>, <institution>KIET Group of Institutions</institution>, <city>Ghaziabad</city> <postal-code>201206</postal-code>, <country>India</country><institution content-type="dept">Department of CSE</institution>, <institution>IMS Engineering College</institution>, <city>Ghaziabad</city> <postal-code>201009</postal-code>, <country>India</country>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.https://www.sciopen.com/article/10.26599/BDMA.2020.9020016coronaviruscovid-19respiratory tractmulti-class classificationrandom forest |
| spellingShingle | Vishan Kumar Gupta Avdhesh Gupta Dinesh Kumar Anjali Sardana Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model Big Data Mining and Analytics coronavirus covid-19 respiratory tract multi-class classification random forest |
| title | Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model |
| title_full | Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model |
| title_fullStr | Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model |
| title_full_unstemmed | Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model |
| title_short | Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model |
| title_sort | prediction of covid 19 confirmed death and cured cases in india using random forest model |
| topic | coronavirus covid-19 respiratory tract multi-class classification random forest |
| url | https://www.sciopen.com/article/10.26599/BDMA.2020.9020016 |
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