Open Access

ARTICLE

Supervised Machine Learning-Based Prediction of COVID-19

by Atta-ur-Rahman¹, Kiran Sultan³, Iftikhar Naseer⁴, Rizwan Majeed⁵, Dhiaa Musleh¹, Mohammed Abdul Salam Gollapalli², Sghaier Chabani², Nehad Ibrahim¹, Shahan Yamin Siddiqui^6,7, Muhammad Adnan Khan^8,*

1 Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
2 Department of Computer Information System, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
3 Department of CIT, Faculty of Applied Studies, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
4 Department of Computer Science & Information Technology, Superior University, Lahore, 54000, Pakistan
5 Directorate of IT, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
6 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
7 Department of Computer Science, Minhaj University Lahore, Lahore, 54000, Pakistan
8 Department of Computer Science, Faculty of Computing, Riphah International University Lahore Campus, Lahore, 54000, Pakistan

* Corresponding Author: Muhammad Adnan Khan. Email:

Computers, Materials & Continua 2021, 69(1), 21-34. https://doi.org/10.32604/cmc.2021.013453

Received 30 August 2020; Accepted 06 December 2020; Issue published 04 June 2021

Abstract

COVID-19 turned out to be an infectious and life-threatening viral disease, and its swift and overwhelming spread has become one of the greatest challenges for the world. As yet, no satisfactory vaccine or medication has been developed that could guarantee its mitigation, though several efforts and trials are underway. Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment. In this regard, healthcare experts, researchers and scientists have delved into the investigation of existing as well as new technologies. The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease. The state-of-the-art research in Artificial intelligence (AI), Machine learning (ML) and cloud computing have encouraged healthcare experts to find effective detection schemes. This study aims to provide a comprehensive review of the role of AI & ML in investigating prediction techniques for the COVID-19. A mathematical model has been formulated to analyze and detect its potential threat. The proposed model is a cloud-based smart detection algorithm using support vector machine (CSDC-SVM) with cross-fold validation testing. The experimental results have achieved an accuracy of 98.4% with 15-fold cross-validation strategy. The comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.

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