Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

• Published in: SN computer science Vol. 2; no. 1; p. 11
• Main Authors: Muhammad, L. J., Algehyne, Ebrahem A., Usman, Sani Sharif, Ahmad, Abdulkadir, Chakraborty, Chinmay, Mohammed, I. A.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.02.2021; Springer Nature B.V
• Subjects: Accuracy; Advances in Computational Approaches for Artificial Intelligence; Agents (artificial intelligence); Algorithms; Alternatives; Artificial intelligence; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Correlation coefficients; COVID-19; Data analysis; Data mining; Data Structures and Information Theory; Datasets; Decision making; Decision trees; Deep learning; Developing countries; Disease transmission; Epidemiology; Health care; Image Processing, IoT and Cloud Applications; Infections; Information Systems and Communication Service; LDCs; Machine learning; Neural networks; Original Research; Pandemics; Pattern Recognition and Graphics; Performance evaluation; Public health; Regression analysis; Respiratory diseases; Severe acute respiratory syndrome coronavirus 2; Signs and symptoms; Software Engineering/Programming and Operating Systems; Supervised learning; Support vector machines; System effectiveness; Viral diseases; Vision; China; COVID-19; Pandemic; Decision tree; Dataset; Machine learning

COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This real...