TY  - JOUR
T1  - Analysis of Mortality-Based Global Health Metrics: A Principle Component Analysis (PCA) – K-Means Approach to Country-Level Data
AU  - Önder, Emrah
AU  - Önder, Güler
AU  - Uslu, Yeter
AU  - Tüzün, Ümran
PY  - 2024
DA  - December
Y2  - 2024
DO  - 10.17093/alphanumeric.1548227
JF  - Alphanumeric Journal
JO  - Alphanumeric
PB  - Muhlis ÖZDEMİR
WT  - DergiPark
SN  - 2148-2225
SP  - 75
EP  - 106
VL  - 12
IS  - 2
LA  - en
AB  - 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.
KW  - Health
KW  - Mortality
KW  - World Development Indicators
KW  - Principal Component Analysis
KW  - Clustering
CR  - Bizzego, A., Gabrieli, G., Bornstein, M. H., Deater-Deckard, K., Lansford, J. E., Bradley, R. H., Costa, M., & Esposito, G. (2021). Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach. International Journal of Environmental Research and Public Health, 18(3), 1315. https://doi.org/10.3390/ijerph18031315
CR  - Cheng, X., Yang, Y., Schwebel, D. C., Liu, Z., Li, L., Cheng, P., Ning, P., & Hu, G. (2020). Population ageing and mortality during 1990–2017: A global decomposition analysis. PLOS Medicine, 17(6), e1003138. https://doi.org/10.1371/journal.pmed.1003138
CR  - Geograp.IN. (2024, March). Color the map. Matika.in z. s. https://www.geograf.in/en/map-color.php
CR  - Hayran, O., & Ozbek, H. (2017). Sag\ul ık bilimlerinde aras\ct ırma ve istatistik yontemler ((SPSS uygulama ornekleri ile genis\cletilmis  ̧2. baskı).). Iṡtanbul.
CR  - Hedegaard, H. (2021). Suicide Mortality in the United States, 19992019. https://doi.org/10.15620/cdc:101761
CR  - Kayode, G. A., Grobbee, D. E., Amoakoh-Coleman, M., Ansah, E., Uthman, O. A., & Klipstein-Grobusch, K. (2017). Variation in neonatal mortality and its relation to country characteristics in sub-Saharan Africa: an ecological study. BMJ Global Health, 2(4), e209. https://doi.org/10.1136/bmjgh-2016-000209
CR  - Kelley, E., & Hurst, J. (2006). https://doi.org/10.1787/440134737301
CR  - Knoema. (2024, March). World Development Indicators. Knoema. https://public.knoema.com/lftihvf/world-development-indicators-wdi
CR  - Liang, C. Y., Kornas, K., Bornbaum, C., Shuldiner, J., De Prophetis, E., Buajitti, E., Pach, B., & Rosella, L. C. (2023). Mortality-based indicators for~measuring health system performance and~population health in high-income countries: a systematic review. IJQHC Communications, 3(2). https://doi.org/10.1093/ijcoms/lyad010
Mohbey, K. K., & Bakariya, B. (2022). An Introduction to Python Programming. BPB Publications.
CR  - Odong, T. L., Heerwaarden, J. van, Hintum, T. J. L. van, Eeuwijk, F. A. van, & Jansen, J. (2013). Improving Hierarchical Clustering of Genotypic Data via Principal Component Analysis. Crop Science, 53(4), 1546–1554. https:// doi.org/10.2135/cropsci2012.04.0215
CR  - Pajankar, A., & Joshi, A. (2022). Handson Machine Learning with Python: Implement Neural Network Solutions with Scikitlearn and PyTorch. Apress. https://doi.org/10.1007/978-1-4842-7921-2
CR  - Ramasubramanian, K., & Singh, A. (2019). Machine Learning Using R: With Time Series and IndustryBased Use Cases in R. Apress. https://doi.org/10.1007/978-1-4842-4215-5
CR  - Ros, F., & Riad, R. (2024). Feature and Dimensionality Reduction for Clustering with Deep Learning. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48743-9
CR  - Sanguansat, P. (2012). Principal Component Analysis  Multidisciplinary Applications. InTech. https://doi.org/10.5772/2694
CR  - Sartorius, B. K., & Sartorius, K. (2014). Global infant mortality trends and attributable determinants – an ecological study using data from 192 countries for the period 1990–2011. Population Health Metrics, 12(1). https:// doi.org/10.1186/s12963-014-0029-6
CR  - UNICEF. (2023). Levels and Trends in Child Mortality (Issue Report2023).
CR  - Vasques, X. (2024). Machine Learning Theory and Applications: Handson Use Cases with Python on Classical and Quantum Machines. Wiley. https://doi.org/10.1002/9781394220649
CR  - Victora, C. G., Barros, A. J. D., Blumenberg, C., Costa, J. C., Vidaletti, L. P., Wehrmeister, F. C., Masquelier, B., Hug, L., & You, D. (2020). Association between ethnicity and under-5 mortality: analysis of data from demographic surveys from 36 low-income and middle-income countries. The Lancet Global Health, 8(3), e352–e361. https://doi.org/10.1016/s2214-109x(20)30025-5
CR  - Ward, Z. J., Atun, R., King, G., Sequeira Dmello, B., & Goldie, S. J. (2023). Simulation-based estimates and projections of global, regional and country-level maternal mortality by cause, 1990–2050. Nature Medicine, 29(5), 1253–1261. https://doi.org/10.1038/s41591-023-02310-x
CR  - WHO. (2024, August). Cancer. World Health Organization. https://www.who.int/health-topics/cancer/#tab=tab\_1
CR  - World Bank. (2024, March). The World Development Indicators. The World Bank. https://datatopics.worldbank.org/world-development-indicators/
UR  - https://doi.org/10.17093/alphanumeric.1548227
L1  - https://dergipark.org.tr/en/download/article-file/4206388
ER  -