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

Autor: Muhammad LJ; Department of Mathematics and Computer Science, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria., Algehyne EA; Department of Mathematics, University of Tabuk, Tabuk, 71491 Saudi Arabia., Usman SS; Department of Biological Sciences, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria., Ahmad A; Department of Computer Science, Kano University of Science and Technology, Wudil, Kano Nigeria., Chakraborty C; Department of Electronics and Communication Engineering, Birla Institute of Technology, Ranchi, Jharkhand India., Mohammed IA; Computer Science Department, Yobe StateUniversity, Damaturu, Yobe State Nigeria.
Jazyk: angličtina
Zdroj: SN computer science [SN Comput Sci] 2021; Vol. 2 (1), pp. 11. Date of Electronic Publication: 2020 Nov 27.
DOI: 10.1007/s42979-020-00394-7
Abstrakt: 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 reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.
Competing Interests: Conflict of interestAuthors have declared that no conflict of interest exists.
(© Springer Nature Singapore Pte Ltd 2020.)
Databáze: MEDLINE
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