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

Önder, Güler; Uslu, Yeter; Tüzün, Ümran; Önder, Emrah

doi:https://doi.org/10.17093/alphanumeric.1548227

ISSN: 2148-2225 (online)

alphanumeric journal

The Journal of Operations Research, Statistics, Econometrics and Management Information Systems

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

Güler Önder

Yeter Uslu

Ümran Tüzün

Emrah Önder

Abstract

In 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.

Keywords: Clustering, Health, Mortality, Principal Components Analysis, World Development Indicators

Jel Classification: C38, I18, O57

Suggested citation

Önder, G., Uslu, Y., Tüzün, Ü. & Önder, E. (2024). Analysis of Mortality-Based Global Health Metrics: A Principle Component Analysis (PCA) – K-Means Approach to Country-Level Data. Alphanumeric Journal, 12(2), 75-106. https://doi.org/10.17093/alphanumeric.1548227

bibtex

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2024.12.02.STAT.01

alphanumeric journal

Volume 12, Issue 2, 2024

Pages 75-106

Received: Sept. 11, 2024

Accepted: Nov. 6, 2024

Published: Dec. 31, 2024

Full Text [2.9 MB]

2024 Önder, G., Uslu, Y., Tüzün, Ü., Önder, E.

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