A Fuzzy Clustering-based Approach for Classifying COVID-19 Patients by Age and Early Symptom Indicators

Abstract

The devastating illness known as Covid-19 has disrupted the lives of individuals all over the globe and left a trail of devastation in its wake. The fact that we are unable to determine the severity of illness (SOI) class of the patient during the early stages of infection is without a doubt the most challenging aspect of this disease. An accurate classifier model has to be constructed in order to ensure that patients diagnosed with Covid-19 get prompt and individualized therapy. Within the scope of this investigation, we propose a useful fuzzy clustering based model for categorizing Covid-19 patients according to their age and the severity of their early symptoms (fever, dry cough, breathing difficulties, headache, smell, and taste disturbance). This method is superior to previous hard clustering tactics in terms of reducing the number of deaths that occur among patients suffering from coronavirus and increasing the likelihood that they will recover fully.