With the wide spread of Coronavirus, most people who infected with the COVID-19, will recover without requiring special treatment. Whereas, elders and those with underlying medical problems are more likely to have serious illnesses, even be threatened with death. Many more disciplines try to find solutions and drive master plan to this global trouble. Consequently, by taking one particular population, Hungary, this study aims to explore a pattern of COVID-19 victims, who suffered from some underlying conditions. Age, gender, and underlying medical problems form the structure of the clustering. K-Means and two step clustering methods were applied for age-based and age-independent analysis. Grouping of the deaths in the form of two different scenarios may highlight some concepts of this deadly disease for public health professionals. Our result for clustering can forecast similar cases which are assigned to any cluster that it will be a serious cautious for the population.