Open Access

ARTICLE

COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

Tahir Sher¹, Abdul Rehman², Dongsun Kim^2,*

1 Department of Creative Technologies, Air University, Islamabad, 44230, Pakistan
2 School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Korea

* Corresponding Author: Dongsun Kim. Email:

Computers, Materials & Continua 2023, 74(1), 1561-1574. https://doi.org/10.32604/cmc.2023.032020

Received 03 May 2022; Accepted 24 June 2022; Issue published 22 September 2022

Abstract

COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well. Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world. However, with the advancement of technology, the Internet of Things (IoT) and social IoT (SIoT), the versatile data produced by smart devices helped a lot in overcoming this lethal disease. Data mining is a technique that could be used for extracting useful information from massive data. In this study, we used five supervised ML strategies for creating a model to analyze and forecast the existence of COVID-19 using the Kaggle dataset” COVID-19 Symptoms and Presence.” RapidMiner Studio ML software was used to apply the Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (K-NNs) and Naive Bayes (NB), Integrated Decision Tree (ID3) algorithms. To develop the model, the performance of each model was tested using 10-fold cross-validation and compared to major accuracy measures, Cohan’s kappa statistics, properly or mistakenly categorized cases and root means square error. The results demonstrate that DT outperforms other methods, with an accuracy of 98.42% and a root mean square error of 0.11. In the future, a devised model will be highly recommendable and supportive for early prediction/diagnosis of disease by providing different data sets.

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Keywords

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