Early-Stage Detection of Covid-19 Patient using ML Model: A Case Study

Authors

Keywords:

Covid-19, Internet of Things (IoT), Machine Learning (ML)

Abstract

The Covid-19 pandemic has drastically changed the daily living style of human beings by astonishing the cultural, educational, regional, business, social, and marketing activities within a limited boundary. It also has impacted the healthcare system globally and provided a lot of burden on the healthcare system. The circumstances that arose due to such a pandemic require a vital solution to deal with it. In such a situation, most innovative technologies have grown up to find alternative solutions to track the situation that arises due to Covid-19.  Among all innovative technologies, IoT can be counted as the best approach to deal with such a type of pandemic due to its associated features of transmitting data from any remote location without human intervention. Such type of technology has the capability of providing connectivity among various medical devices either in hospitals or other deliberate places to deal with such type of pandemic. First of all, this paper introduces the concept of IoT to deal with the circumstances of the Covid-19 pandemic. Along with that, a framework of a real-time Covid-19 patient monitoring system has been proposed in this paper that can be utilized in the future. The proposed framework helps in monitoring the symptoms of Covid-19 infected patients. On the basis of that model, a case study is done on Covid-19 symptom data by using different ML algorithms. The findings indicate that all algorithms achieved an accuracy of more than 80% and RFT achieved the highest accuracy of 92%. Based on these findings, we believe that these algorithms will produce efficient and precise outcomes when applied to real-time symptom data.

Downloads

Download data is not yet available.

References

J.Page., D.Hinshaw., B.McKay, “In Hunt for Covid-19 Origin, Patient Zero Points to Second Wuhan Market – The man with the first confirmed infection of the new coronavirus told the WHO team that his parents had shopped there,” The Wall Street Journal, 26 Feb. 2021.

C.Zimmer, “The Secret Life of a Coronavirus – An oily, 100-nanometer-wide bubble of genes has killed more than two million people and reshaped the world. Scientists don’t quite know what to make of it,26 February 2021.

M.A.Islam, “Prevalence and characteristics of fever in adult and paediatric patients with coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis of 17515 patients,” PLOS ONE., vol. 16, no. 4, 2021.

Ministry of Health and Welfare, Government of India. COVID-19 India. Accessed June 9, 2020.

J.Siaya.Sania, M.A.Islam,” Prevalence and Characteristics of Taste Disorders in Cases of COVID-19: A Meta-analysis of 29,349 Patients. Otolaryngology-Head and Neck Surgery, “vol. 165, issue 1, pp. 33-42, 2021 Jul, DOI: 10.1177/0194599820981018.

New York Post, The Most Promising Coronavirus Breakthroughs So Far, from Vaccines to Treatments, 2020. April 8, https://nypost.com/2020/04/08/coronav virus-breakthroughs-how-close-are-we-to-a-vaccine/.

M.P. Kelly,” Digital technologies and disease prevention,“Am. J. Prev. Med., vol. 51, no. 5, pp. 861-866, 2016. DOI: https://doi.org/10.1016/j.amepre.2016.06.012.

P.M. Hlaing, T.R. Nopparatjamjomras, S. Nopparatjamjomras, “Digital technology for preventative health care in Myanmar,“Digital Medicine, vol.4, no. 3, pp. 117–121, 2018 https://doi.org/10.4103/digm.digm_25_18.

Michael Safi "India's shocking surge in Covid cases follows baffling decline, " The Guardian. Retrieved 29 April 2021.

"IndiaFightsCorona COVID-19". MyGov.in. Govt of India. 16 March 2020.

Daily COVID-19 vaccine doses administered – India, Our World in Data. Retrieved 13 May 2021.

Navita Mehra and Pooja Mittal, “Development of ML and IoT Enabled Disease Diagnosis Model for a Smart Healthcare System” Int. J. of Comp. Sci.& Net. Sec. (IJCSNS), vol. 22 No.7, July 2022, ISSN: 1738-7906.

M.A. Alzubaidi, M. Otoom, N.Otoum, Y. Etoom, R.Banihani, “A novel computational method for assigning weights of importance to symptoms of COVID-19 patients,”Artif Intell Med. 2021 Feb;112:102018. doi: 10.1016/j.artmed.2021.102018.

J. Medina, M. Espinilla, A.L. ´ García-Fern´ and, L. Martínez, “Intelligent multi-dose medication controller for fever: from wearable devices to remote dispensers,”Comput. Electrical Eng.vol.65, pp. 400–412, 2018.

Y. Umayahara, Z. Soh, K. Sekikawa, T. Kawae, A. Otsuka, T. Tsuji, “A mobile cough strength evaluation device using cough sounds,”Sensors, vol. 18, 2018.

D. Ichwana, R.Z. Ikhlas, S. Ekariani, “Heart rate monitoring system during physical exercise for fatigue warning using non-invasive wearable sensor, in: 2018,”Int. Conf. on Info. Tech. Sys. and Innovation (ICITSI), Bandung - Padang, Indonesia, pp. 497–502, 2018.

B. Askarian, S.-H. Yoo, J.W. Chong, Novel image processing method for detecting strep throat (streptococcal pharyngitis) using smartphone, Sensors 19 (15) (2019) 3307.

A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, “Internet of things: a survey on enabling technologies, protocols, and applications,”IEEE Commun. Surveys Tutorials vol. 17, no. 4, pp. 2347–2376, 2015.

E. Ahmed, I. Yaqoob, I.A.T. Hashem, I. Khan, A.I.A. Ahmed, M. Imran, A. V. Vasilakos, “The role of big data analytics in Internet of Things, “Comput. Netw., vol. 129, pp. 459–471, 2017.

C. Mustafa, S. Askar, “Machine Learning for IoT HealthCare Applications: A Review”, International Journal of Science and Business (IJSAB), vol. 5, no. 3, pp. 42-51, 2021.

AAldahiri, B. Alrashed, W.Hussain,,”Trends in Using IoT with Machine Learning in Health Prediction System”, Forecasting, vol. 3, pp. 181-207, 2021. DO: https://doi.org/10.3390/forecast301001

Covid-19 Patient Monitoring Device based on LoRa - Arduino Project Hub

H.Banaee, M.U.Ahmed., A.Loutfi.,” Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges,” Sensors (Basel), vol. 13, no. 12, pp.17472-17500, 2013.

P.Dutta., S.Paul, and A. Kumar,” Comparative analysis of various supervised machine learning techniques for the diagnosis of COVID-19,” Electronic Devices, Circuits, and Systems for Biom. App., pp. 521–540, 2021.

Z.Zhu., Z.Xingming, G. Tao, T.Dan., J.Chen. Li, I. Zhou, Z.Zhang, X. Zhou., D.Chen., H.Wen. and H. Cai.,” Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort,”Interdisciplinary Sci. pp.73-82, 2021.

T.Yan, P.K.Wong, H.Ren, H. Wang., J.Wang, Y.Li.” Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans,”Chaos Solitons Fractals, 2020.

A. Darwish, A.E.Hassanien, M. Elhoseny, et al.,” The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems,” J Ambient Intell Human Comput 10, pp. 4151–4166 2019.

IoT based Covid-19 Patient Monitoring Framework using different Wearable Sensors [12]

Downloads

Published

16.01.2023

How to Cite

[1]
P. . Mittal and Navita, “Early-Stage Detection of Covid-19 Patient using ML Model: A Case Study”, Int J Intell Syst Appl Eng, vol. 11, no. 1s, pp. 84–89, Jan. 2023.