Analysis of Mortality-Based Global Health Metrics: A Principle Component Analysis (PCA) – K-Means Approach to Country-Level Data

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dc.contributor.authorÖnder, Güler
dc.contributor.authorUslu, Yeter
dc.contributor.authorTüzün, Ümran
dc.contributor.authorÖnder, Emrah
dc.date.accessioned2025-03-26T13:52:48Z
dc.date.available2025-03-26T13:52:48Z
dc.date.issued2024
dc.departmentİstanbul Esenyurt Üniversitesi
dc.description.abstractIn this study, principal component analysis and the k-means algorithm were employed for the analysis of Mortality-Based Global Health Metrics. The aim of this study is to create homogeneous clusters of countries in terms of Mortality-Based Global Health Metrics, to identify similar countries within clusters using within-cluster exploratory data analysis methods, and to investigate the common characteristics of these countries. At the country level, a dataset comprising 34 indicators was compiled. However, due to the curse of dimensionality inherent in machine learning, the dataset was reduced to 6 principal components through principal component analysis (PCA). Countries were then clustered into 6 groups using the K-means clustering analysis method. The elbow method and silhouette method were utilized for optimal cluster selection. The cluster information resulting from dimensionality reduction analysis and clustering analysis can serve as a valuable input for policymakers in healthcare, particularly regarding cluster centroids and the countries constituting each cluster. Healthcare policymakers for each country can develop much more rational policies in their decision-making processes by evaluating their own countries, other countries within the same cluster, the characteristic features of their own clusters, and the distances to successful cluster centroids. This enables better examination of positive and negative indicators in country comparisons.
dc.identifier.doi10.17093/alphanumeric.1548227
dc.identifier.doihttps://doi.org/10.17093/alphanumeric.1548227
dc.identifier.endpage106
dc.identifier.issn2148-2225
dc.identifier.issue2
dc.identifier.startpage75
dc.identifier.urihttps://hdl.handle.net/20.500.14704/259
dc.identifier.volume12
dc.language.isoen
dc.publisherMuhlis ÖZDEMİR
dc.relation.ispartofAlphanumeric Journal
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20250326
dc.subjectHealth
dc.subjectMortality
dc.subjectWorld Development Indicators
dc.subjectPrincipal Component Analysis
dc.subjectClustering
dc.titleAnalysis of Mortality-Based Global Health Metrics: A Principle Component Analysis (PCA) – K-Means Approach to Country-Level Data
dc.typeReview Article

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