The Coronavirus Disease 2019 (COVID19) has brought severe stress on all human lives, especially to healthcare systems worldwide. The Healthcare system includes personnel, equipment and infrastructure, medicines, and voluminous dataset. This is a contagious and dreadful disease that timely treatment must be given to cure. All the patients affected by coronavirus cannot be treated the same way and provide the same resource. Depending on the severity of the disease, treatment should be given and resources should be allocated. Doctors and healthcare personnel interrogating the patients and identifying them based on their severity is a time-consuming process. In such a scenario, the help of IT and computer are the need of the hour. A computer-based system is needed that automatically categorizes the patients according to their severity. The machine learning approach will be best suited to category coronavirus-affected patients based on their severity. Unsupervised learning is a technique that categorizes the dataset without the label. Clustering is one of the techniques of unsupervised learning. There are ample number of clustering algorithms available in the market for this purpose. K-means algorithm is one of the clustering algorithms that is simple and efficient to use. This research project aims at improving the performance of the K-means clustering algorithm that categorizes the data more accurately.

k-means clustering, covid19 dataset, machine learning, clustering algorithm