Machine Learning in Global Development: Applying k-Means Clustering to Identify Country Groupings by Economic and Health Performance

Section: Article
Issue
Vol. 35 No. 1 (2026): Volume 35, Issue 1
Published
Jan 1, 2026
Pages
28-37

Abstract

This study applies k-means clustering to group countries based on key economic and health indicators: Gross National Income per capita (GNIP), health expenditure, life expectancy, birth and death rates, and urbanization. The elbow method identified k = 3 as the optimal number of clusters, indicating a significant drop in within-cluster sum of squares (from 5000 to 2000). The results reveal three distinct development groupings. A small cluster of 13 high-performing countries stands out with strong economic (GNIP = 0.658) and health outcomes (Life Expectancy = 0.856), along with low birth (0.116) and death rates (0.113). This group also shows strong internal similarity (silhouette width = 0.58). The remaining countries fall into two broader clusters. The first includes 320 countries with moderate development, higher urbanization (UrbanP = 0.712), and relatively high health spending (healthE = 0.219), but lower GNIP (0.066). The second cluster of 277 countries faces greater challenges, marked by low life expectancy (0.414), high birth rates (0.670), and weak economic indicators (GNIP = 0.067). Both larger clusters show moderate cohesion (silhouette widths = 0.29 and 0.32). These findings highlight the stratified and multidimensional nature of global development, offering a data-driven framework to inform policy decisions and tailor interventions to the unique characteristics of each cluster.

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Authors

O. P. Adebayo

Department of Statistics, Phoenix University Agwada, Nasarawa State, Nigeria

I Ahmed

Department of Statistics, Nasarawa State University Keffi, Nasarawa State, Nigeria

I.M. Garba

Department of Agriculture, Phoenix University Agwada, Nasarawa State, Nigeria

K.T. Oyeleke

Department of Statistics, Olabisi Onabanjo University Ago Iwoye, Ogun State, Nigeria

Identifiers

https://doi.org/10.33899/jes.v35i1.60269
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[1]
O. P. . Adebayo, I. Ahmed, I. . Garba, and K. . Oyeleke, “Machine Learning in Global Development: Applying k-Means Clustering to Identify Country Groupings by Economic and Health Performance”, JES, vol. 35, no. 1, pp. 28–37, Jan. 2026.
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