jista

Journal of Intelligent Systems: Theory and Applications

2651-3927

Özer UYGUN

10.38016/jista.1365609

Neural Networks Semi- and Unsupervised Learning

Nöral Ağlar Yarı ve Denetimsiz Öğrenme

SOM Clustering of OECD Countries for COVID-19 Indicators and Related Socio-economic Indicators

https://orcid.org/0000-0002-5919-1986

Yıgıt

Pakize

İSTANBUL MEDİPOL ÜNİVERSİTESİ, TIP FAKÜLTESİ

09 26 2024

7 2 95 101 09 25 2023 04 05 2024

2018

Journal of Intelligent Systems: Theory and Applications

The coronavirus disease is one of the most severe public health problems globally. Governments need policies to better cope with the disease, so policymakers analyze the country's indicators related to the pandemic to make proper decisions. The study aims to cluster OECD (Organisation for Economic Co-operation and Development) countries using COVID-19, health, socioeconomic, and environmental indicators. A self-organizing map (SOM) clustering method, an unsupervised artificial neural network (ANN) method and a hierarchical clustering method are used. The data comprises 38 OECD countries, and 16 different variables are selected. As a result, the countries are grouped into 3 clusters. Cluster 1 contains 33 countries, the USA is Cluster 2, and Cluster 3 has 4 countries, including Turkey. COVID-19 mortality is highly related to mortality from chronic respiratory diseases. In addition, environmental indicators show differences in clusters.

COVID-19 OECD countries Kohonen SOM clustering

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