Open Access

ARTICLE

Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

by Omer Ali^1,2, Mohamad Khairi Ishak^1,*, Muhammad Kamran Liaquat Bhatti²

1 School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal, 14300, Malaysia
2 Department of Electrical Engineering, NFC Institute of Engineering and Technology (NFC IET), Multan, 60000, Pakistan

* Corresponding Author: Mohamad Khairi Ishak. Email:

(This article belongs to the Special Issue: Recent Trends in Machine Intelligence respected to Medical Field Applications)

Computers, Materials & Continua 2021, 69(1), 747-766. https://doi.org/10.32604/cmc.2021.018098

Received 24 February 2021; Accepted 05 April 2021; Issue published 04 June 2021

Abstract

The COVID-19 virus exhibits pneumonia-like symptoms, including fever, cough, and shortness of breath, and may be fatal. Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities. Clinical studies help to make a correct diagnosis of COVID-19, where the disease has already spread to the organs in most cases. Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread. Therefore, clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold, which are worth investigating. Current supervised Machine Learning (ML) based techniques mostly investigate clinical reports such as X-Rays and Computerized Tomography (CT) for disease detection. This strategy relies on a larger clinical dataset and does not focus on early symptom identification. Towards this end, an innovative hybrid unsupervised ML technique is introduced to uncover the probability of COVID-19 occurrence based on the breathing patterns and commonly reported symptoms, fever, and cough. Specifically, various metrics, including body temperature, breathing and cough patterns, and physical activity, were considered in this study. Finally, a lightweight ML algorithm based on the K-Means and Isolation Forest technique was implemented on relatively small data including 40 individuals. The proposed technique shows an outlier detection with an accuracy of 89%, on average.

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Keywords

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