Machine Learning Based Clustering of COVID-19 Symptoms

Coronavirus disease (COVID-19) is an infectious respiratory illness caused by Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2). The virus is new and has caused an unprecedented pandemic that has led into unforeseeable circumstances. Due to lack of knowledge, medical fraternity is facing a lot of hurdles in the detection and treatment of this disease which is caused by coronavirus. It is immensely crucial to handle the prevailing situation for the sake of healthier lives in future. In present scenario, data recorded from the occurrences of the disease worldwide can play a vital role in creating a better understanding of its nature. Based on data, artificial intelligence techniques like machine learning provide useful insights and solutions for real world problems that are otherwise incomprehensible for human. Specifically, clustering is an unsupervised machine learning technique that uses unlabelled data to identify different behaviour, groups and categories of observations. This paper presents a machine learning based clustering approach to identify different clusters of symptomatic profiles of patients suffering from covid-19. Dividing the set of medical symptoms experienced by covid-19 patients into distinct clusters will facilitate an understanding of the different kinds of effects this disease has on human beings. It will also enable development of suitable and effective treatments.