The pandemic, which frightened the whole world, was reported in December
2019 as mass pneumonia cases in Wuhan city of China. The fact that the deadly new
type of coronavirus can be transmitted extremely easily from person to person has also
increased the spread of the disease. This spread negatively affects social, economic,
and demographic life all over the world. This study aimed to identify which chronic
and other diseases in combination with COVID-19 caused mortalities around the globe.
As a result of the analysis, the appropriateness of the random effect unbalanced panel
data model to the research purpose was determined. Coronavirus deaths related to the
results of the Wald test used in the Generalized Least Squares (GLS) Technique,
cardiovascular, diabetes, hypertension, respiratory disease, cancer, and other diseases
are significant. In addition, the hierarchical clustering technique was applied to the
meaningful model. According to the Ward Technique results, countries with similar
chronic and other diseases for Coronavirus-related deaths were included in the same
cluster.
On the other hand, the multi-layered Perceptron (MLP) model, one of the Artificial
Neural Network (ANN) methods, was applied to the same model. The aim is to
determine which chronic disease has a more significant effect on the Coronavirusrelated
death factor. Literature research shows that hypertension disease ranks first in
Corona-related deaths worldwide. The analysis of the MLP model made for this
purpose determined that hypertension disease was in the first place in pandemic deaths.
Keywords: Artificial Neural Networks, Clustering, COVID-19, Panel Model,
WHO.
