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Journals / Alphanumeric Journal / 2024 / Volume: 12 Issue: 2

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

Güler Önder(ISTANBUL ESENYURT UNIVERSITY)0000-0002-0879-9545
Yeter Uslu(ISTANBUL MEDIPOL UNIVERSITY)0000-0002-8529-6466
Ümran Tüzün(Istanbul University, School of Business, Department of Quantitative Methods)0009-0007-8741-727X
Emrah Önder(Istanbul University, School of Business, Department of Quantitative Methods)0000-0002-0554-1290
Pages
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Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This enables better examination of positive and negative indicators in country comparisons.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, principal component analysis and the k-means algorithm were employed for the analysis of Mortality-Based Global Health Metrics.
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Key Findings & Data:At the country level, a dataset comprising 34 indicators was compiled.
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Conclusion & Impact:This enables better examination of positive and negative indicators in country comparisons.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Health · Mortality · World Development Indicators

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.

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Cite This Article

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, Volume: 12 Issue: 2. https://doi.org/10.17093/alphanumeric.1548227

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