Header menu link for other important links
Applying ML on COVID-19 Data to Understand Significant Patterns
Ghumare T., Ghorpade V.,
Published in Springer Science and Business Media Deutschland GmbH
Volume: 116
Pages: 513 - 525
Corona viruses are a genus of viruses that infect vertebrate and birds, causing a variety of diseases. They induce a variety of respiratory problems in people. This study investigates COVID-19 infection rates and estimates the pandemic’s scope, recovery rate, and death rate. We used Support Vector Machine (SVM), Random Forest, Decision Tree, K-nearest neighbor, and other well-known machine learning and mathematical modeling approaches. For disease diagnosis, study used three unique disease data sets (Asthma, Diabetes, and AIDS) provided there in UCI machine learning repository. After getting positive results, we applied these algorithms on COVID-19 data set. We used several categorization algorithms, each with its own set of benefits. The study’s findings support the use of machine learning in disease early detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About the journal
JournalLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Open AccessNo