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



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


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.


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




How to Cite

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.