  COVID-19 hits the world like a storm by arising pandemic situations for most
of the countries around the world. The whole world is trying to overcome this
pandemic situation. A better health care quality may help a country to tackle
the pandemic. Making clusters of countries with similar types of health care
quality provides an insight into the quality of health care in different
countries. In the area of machine learning and data science, the K-means
clustering algorithm is typically used to create clusters based on similarity.
In this paper, we propose an efficient K-means clustering method that
determines the initial centroids of the clusters efficiently. Based on this
proposed method, we have determined health care quality clusters of countries
utilizing the COVID-19 datasets. Experimental results show that our proposed
method reduces the number of iterations and execution time to analyze COVID-19
while comparing with the traditional k-means clustering algorithm.
