Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 10 , Issue 2 , PP: 20-31, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Noor Razzaq Abbas 1 * , Ghassan AL-Thabhawee 2 , Isam Bahaa Aldallal 3 , Mostafa Abotaleb 4 , Klodian Dhoska 5
Doi: https://doi.org/10.54216/JAIM.100202
Hierarchical clustering is applied in this research to study world COVID-19 data up to January 2025 and partition the primary clusters of countries based on epidemiological criteria. Total cases, deaths, recoveries, active cases, tests, population, and per-million were the data explored and were standardized and thereafter analyzed employing agglomerative hierarchical clustering with Ward linkage. The assessment yielded an average Silhouette of 38.5%, Davies–Bouldin value of 0.87, and Calinski–Harabasz value of 77.6, reflecting cluster validity in separation. The application of dendrograms and PCA projections to plot identified four clusters, reflecting differences in the severity of COVID-19 impacts and responses. Clustering analysis revealed that the high-burden clusters accounted for almost 45% of global death, while low-burden clusters were predominant in over 40% of nations with fewer than 100,000 accumulated instances. The outcomes illustrate hierarchical clustering as an unsupervised learning approach to analyzing epidemiological data and give quantitative estimates to facilitate comparative public health interventions across communities.
Hierarchical Clustering , COVID-19 , Agglomerative Clustering , Silhouette Score , PCA , Epidemiology , Machine Learning , Unsupervised Learning , Cluster Analysis , Global Health
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