Open Access

ARTICLE

Determination of COVID-19 Patients Using Machine Learning Algorithms

Marium Malik¹, Muhammad Waseem Iqbal^1,*, Syed Khuram Shahzad², Muhammad Tahir Mushtaq², Muhammad Raza Naqvi^3,4, Maira Kamran¹, Babar Ayub Khan⁴, Muhammad Usman Tahir⁴

1 Department of Software Engineering, The Superior College, Lahore, 54000, Pakistan
2 Department of Informatics and Systems, University of Management and Technology, Lahore, 54000, Pakistan
3 INP-ENIT, University of Toulouse, 65000, France
4 Department of Computer Science, The Superior College, Lahore, 54000, Pakistan

* Corresponding Author: Muhammad Waseem Iqbal. Email:

Intelligent Automation & Soft Computing 2022, 31(1), 207-222. https://doi.org/10.32604/iasc.2022.018753

Received 20 March 2021; Accepted 18 May 2021; Issue published 03 September 2021

Abstract

Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied . The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naïve bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms.

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