Artificial Neural Networks Approach in Determining Factors of Death Caused by Coronavirus in the World with Unbalanced Panel Data Models

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.