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Ölüm Nedenlerine Göre K-Ortalamalar Yöntemi İle Ülkelerin Kümelenmesi

Year 2020, Volume: 8 Issue: 1, 111 - 130, 30.06.2020
https://doi.org/10.17093/alphanumeric.588835

Abstract

Ölüm nedenleri, ülkelerin sağlık
sistemlerinin değerlendirilmesi ve İnsani Gelişme Düzeylerinin belirlenmesinde
kullanılan ölçütlerden birisidir. Ülkeler, ölüm nedenlerine bağlı olarak sağlık
politikaları geliştirmektedirler. Ölüm oranları ve ölüm nedenleri Birleşmiş
Milletler tarafından ülkeler için gelişmişlik göstergeleri arasında kabul
edilirken, toplum sağlığının iyileştirilmesi de küresel ölçekte bir hedef
olarak gösterilmektedir. Sağlık Ölçümleri ve Değerlendirme Enstitüsü (Institute
for Health Metrics and Evaluation) verilerine göre 2015 yılında 54.15 milyon
ölüm meydana gelmiş ve bu ölümlerin %71’i bulaşıcı olmayan hastalık, %20’si
bulaşıcı hastalıklar, yeni doğan ve beslenme hastalıkları, kalan %9’u ise yaralanmalardan
kaynaklanmıştır. Mevcut çalışmada, farklı ülkelerdeki kişilerin çeşitli ölüm
nedenleri dikkate alınarak ülkelerin gruplandırılması ve ölüm nedenleri ile ülkelerin
İnsani Gelişme Düzeyi arasında bir ilişkinin olup olmadığının incelenmesi
amaçlanmıştır. Analizde; 168 ülke ve bu ülkelerin ölüm nedenlerini gösteren 28
farklı değişkenin 2015 yılı verileri kullanılmıştır. Ülkelerin ölüm nedenlerine
göre gruplanması amacıyla k-ortalamalar yöntemi kullanılmış olup, Dünya Sağlık
Örgütü’nün hastalık, yaralanma ve ölüm nedenlerini sınıflandırmasından faydalanılarak
4 farklı model kurulmuştur. Kümeleme analizinden sonra ülkelerin İnsani Gelişme
Düzeylerine göre hangi kümede yer aldıkları incelenmiştir. Ayrıca ölüm
nedenleri ile ülkelerin İnsani Gelişme Düzeyi arasında bir ilişki olup olmadığı
da araştırılmıştır.

References

  • Acemoglu, D., & Johnson, S. (2007). Disease and development: the effect of life expectancy on economic growth. Journal of political Economy, 115(6), 925-985.
  • Arora, P., Deepali & Varshney, S. (2016). Analysis of k-means and k-medoids algorithm for big data. Procedia Computer Science, 78, 507-512.
  • Bloom, D. E., Canning, D., Kotschy, R., Prettner, K. & Schünemann, J. (2018). Health and Economic Growth: Reconciling the Micro and Macro Evidence. IZA Discussion Papers, No. 11940, Institute of Labor Economics (IZA), Bonn
  • Boutayeb, A., & Boutayeb, S. (2005). The burden of non communicable diseases in developing countries. International journal for equity in health, 4(1), 2.
  • Charrad, M., Ghazzali, N., Boiteau, V., Niknafs, A., & Charrad, M. M. (2014). Package ‘nbclust’. Journal of statistical software, 61, 1-36.
  • Demiralay, M., & Çamurcu, A. Y. (2005). Cure, agnes ve k-means algoritmalarındaki kümeleme yeteneklerinin karşılaştırılması. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 4(8), 1-18.
  • Dhanachandra, N., Manglem, K., & Chanu, Y. J. (2015). Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, 54, 764-771.
  • Hill, K. (2006). Making deaths count. Bulletin of the World Health Organization, 84, 162-162.
  • Kassambara, A., & Mundt, F. (2017). Package ‘factoextra’. Extract and visualize the results of multivariate data analyses, 76.
  • Khan, S. S., & Ahmad, A. (2004). Cluster center initialization algorithm for K-means clustering. Pattern recognition letters, 25(11), 1293-1302.
  • Kırmızıgül Çalışkan, S., & Soğukpınar, İ. (2008). KxKNN: K-Means ve K En Yakın Komşu Yöntemleri ile Ağlarda Nüfuz Tespiti. 2. Ağ ve Bilgi Güvenliği Ulusal Sempozyumu, 16-18.
  • Li, Y., & Wu, H. (2012). A clustering method based on K-means algorithm. Physics Procedia, 25, 1104-1109.
  • Liu, G., Yang, J., Hao, Y., & Zhang, Y. (2018). Big data-informed energy efficiency assessment of China industry sectors based on K-means clustering. Journal of cleaner production, 183, 304-314.
  • Lorentzen, P., McMillan, J., & Wacziarg, R. (2008). Death and development. Journal of economic growth, 13(2), 81-124.
  • Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., ... & AlMazroa, M. A. (2012). Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. The lancet, 380(9859), 2095-2128.
  • Magnusson, R. S. (2007). Non-communicable diseases and global health governance: enhancing global processes to improve health development. Globalization and Health, 3(1), 2.
  • Ritchie H., & Roser M. (2019). Causes of Death. OurWorldInData.org. https://ourworldindata.org/causes-of-death (Erişim Tarihi: 27.03.2019)
  • Sarıman, G. (2011). Veri madenciliğinde kümeleme teknikleri üzerine bir çalışma: k-means ve k-medoids kümeleme algoritmalarının karşılaştırılması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 15(3), 192-202.
  • United Nations Development Programme. (2018). Human development indices and indicators: 2018 Statistical update. http://hdr.undp.org/sites/default/files/hdr2018_technical_notes.pdf (Erişim tarihi: 11.05.2018)
  • United Nations Development Programme. (t.y.). Human Development Data (1990-2017). http://hdr.undp.org/en/data ((Erişim tarihi: 11.05.2018)
  • United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. Resolution adopted by the General Assembly. https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf (Erişim tarihi: 11.05.2018)
  • United Nations. (t.y.). Good Health and Well-Beıng: Why It Matters. https://www.un.org/sustainabledevelopment/wp-content/uploads/2017/03/ENGLISH_Why_it_Matters_Goal_3_Health.pdf (Erişim tarihi: 15.05.2018)
  • Velmurugan, T., & Santhanam, T. (2011). A Survey of Partition based Clustering Algorithms in Data Mining: An experimental approach. Information Technology Journal, 10(3), 478-484.
  • World Health Organization. (2003). Macroeconomics and health: an update: increasing investments in health outcomes for the poor: second consultation on macroeconomics and health (No. WHO/SDE/CMH/03.1). Geneva: World Health Organization.
  • World Health Organization. (2008). The global burden of disease: 2004 update. https://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf (Erişim tarihi: 11.05.2018)
  • World Health Organization. (2018). Global Health Estimates 2016: Disease burden by Cause, Age, Sex, by Country and by Region, 2000-2016. https://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html (Erişim tarihi: 13.05.2018)
  • Yu, S. S., Chu, S. W., Wang, C. M., Chan, Y. K., & Chang, T. C. (2018). Two improved k-means algorithms. Applied Soft Computing, 68, 747-755.
  • Yürük, F., & Erdoğmuş, P. (2015). Düzce İlinin Hayvansal Atıklardan Üretilebilecek Biyogaz Potansiyeli Ve K-Means Kümeleme İle Optimum Tesis Konumunun Belirlenmesi. İleri Teknoloji Bilimleri Dergisi, 4(1), 47-56.

Clustering Countries By K-Means Method According To Causes Of Death

Year 2020, Volume: 8 Issue: 1, 111 - 130, 30.06.2020
https://doi.org/10.17093/alphanumeric.588835

Abstract

Causes of
death are one of the criteria used to assess countries’ health systems and
determine their Human Development Levels. Countries are developing health
policies based on the causes of death. While mortality rates and causes of
death are accepted as development indicators for countries by the United
Nations, improvement of public health is considered as a global target.
According to the Institute for Health Metrics and Evaluation, 54.15 million
deaths occurred in 2015, 71% of which were caused by non-communicable diseases,
20% were caused by communicable diseases, neonatal and nutritional diseases,
and the remaining 9% were caused by injuries. In this study, it is aimed to
group the countries by considering various causes of death of people in
different countries and to investigate whether there is a relationship between
the causes of death and the Human Development Level of the countries. In the
analysis; 2015 data of 168 countries and 28 different variables showing the
causes of death of these countries were used. K-means method was used to group
the countries according to causes of death and 4 different models were
established by making use of World Health Organization's classification of
illness, injury and causes of death. After the cluster analysis, in which
clusters the countries are located according to Human Development Level were
examined. It is also investigated that whether there is a relationship between
the causes of death and the Human Development Level of the countries.

References

  • Acemoglu, D., & Johnson, S. (2007). Disease and development: the effect of life expectancy on economic growth. Journal of political Economy, 115(6), 925-985.
  • Arora, P., Deepali & Varshney, S. (2016). Analysis of k-means and k-medoids algorithm for big data. Procedia Computer Science, 78, 507-512.
  • Bloom, D. E., Canning, D., Kotschy, R., Prettner, K. & Schünemann, J. (2018). Health and Economic Growth: Reconciling the Micro and Macro Evidence. IZA Discussion Papers, No. 11940, Institute of Labor Economics (IZA), Bonn
  • Boutayeb, A., & Boutayeb, S. (2005). The burden of non communicable diseases in developing countries. International journal for equity in health, 4(1), 2.
  • Charrad, M., Ghazzali, N., Boiteau, V., Niknafs, A., & Charrad, M. M. (2014). Package ‘nbclust’. Journal of statistical software, 61, 1-36.
  • Demiralay, M., & Çamurcu, A. Y. (2005). Cure, agnes ve k-means algoritmalarındaki kümeleme yeteneklerinin karşılaştırılması. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 4(8), 1-18.
  • Dhanachandra, N., Manglem, K., & Chanu, Y. J. (2015). Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, 54, 764-771.
  • Hill, K. (2006). Making deaths count. Bulletin of the World Health Organization, 84, 162-162.
  • Kassambara, A., & Mundt, F. (2017). Package ‘factoextra’. Extract and visualize the results of multivariate data analyses, 76.
  • Khan, S. S., & Ahmad, A. (2004). Cluster center initialization algorithm for K-means clustering. Pattern recognition letters, 25(11), 1293-1302.
  • Kırmızıgül Çalışkan, S., & Soğukpınar, İ. (2008). KxKNN: K-Means ve K En Yakın Komşu Yöntemleri ile Ağlarda Nüfuz Tespiti. 2. Ağ ve Bilgi Güvenliği Ulusal Sempozyumu, 16-18.
  • Li, Y., & Wu, H. (2012). A clustering method based on K-means algorithm. Physics Procedia, 25, 1104-1109.
  • Liu, G., Yang, J., Hao, Y., & Zhang, Y. (2018). Big data-informed energy efficiency assessment of China industry sectors based on K-means clustering. Journal of cleaner production, 183, 304-314.
  • Lorentzen, P., McMillan, J., & Wacziarg, R. (2008). Death and development. Journal of economic growth, 13(2), 81-124.
  • Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., ... & AlMazroa, M. A. (2012). Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. The lancet, 380(9859), 2095-2128.
  • Magnusson, R. S. (2007). Non-communicable diseases and global health governance: enhancing global processes to improve health development. Globalization and Health, 3(1), 2.
  • Ritchie H., & Roser M. (2019). Causes of Death. OurWorldInData.org. https://ourworldindata.org/causes-of-death (Erişim Tarihi: 27.03.2019)
  • Sarıman, G. (2011). Veri madenciliğinde kümeleme teknikleri üzerine bir çalışma: k-means ve k-medoids kümeleme algoritmalarının karşılaştırılması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 15(3), 192-202.
  • United Nations Development Programme. (2018). Human development indices and indicators: 2018 Statistical update. http://hdr.undp.org/sites/default/files/hdr2018_technical_notes.pdf (Erişim tarihi: 11.05.2018)
  • United Nations Development Programme. (t.y.). Human Development Data (1990-2017). http://hdr.undp.org/en/data ((Erişim tarihi: 11.05.2018)
  • United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. Resolution adopted by the General Assembly. https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf (Erişim tarihi: 11.05.2018)
  • United Nations. (t.y.). Good Health and Well-Beıng: Why It Matters. https://www.un.org/sustainabledevelopment/wp-content/uploads/2017/03/ENGLISH_Why_it_Matters_Goal_3_Health.pdf (Erişim tarihi: 15.05.2018)
  • Velmurugan, T., & Santhanam, T. (2011). A Survey of Partition based Clustering Algorithms in Data Mining: An experimental approach. Information Technology Journal, 10(3), 478-484.
  • World Health Organization. (2003). Macroeconomics and health: an update: increasing investments in health outcomes for the poor: second consultation on macroeconomics and health (No. WHO/SDE/CMH/03.1). Geneva: World Health Organization.
  • World Health Organization. (2008). The global burden of disease: 2004 update. https://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf (Erişim tarihi: 11.05.2018)
  • World Health Organization. (2018). Global Health Estimates 2016: Disease burden by Cause, Age, Sex, by Country and by Region, 2000-2016. https://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html (Erişim tarihi: 13.05.2018)
  • Yu, S. S., Chu, S. W., Wang, C. M., Chan, Y. K., & Chang, T. C. (2018). Two improved k-means algorithms. Applied Soft Computing, 68, 747-755.
  • Yürük, F., & Erdoğmuş, P. (2015). Düzce İlinin Hayvansal Atıklardan Üretilebilecek Biyogaz Potansiyeli Ve K-Means Kümeleme İle Optimum Tesis Konumunun Belirlenmesi. İleri Teknoloji Bilimleri Dergisi, 4(1), 47-56.
There are 28 citations in total.

Details

Primary Language Turkish
Subjects Operation
Journal Section Articles
Authors

Cem Gürler 0000-0001-5127-6726

Mehmet Çağlar 0000-0002-6859-8972

Onur Önay 0000-0002-7391-5944

Publication Date June 30, 2020
Submission Date July 8, 2019
Published in Issue Year 2020 Volume: 8 Issue: 1

Cite

APA Gürler, C., Çağlar, M., & Önay, O. (2020). Ölüm Nedenlerine Göre K-Ortalamalar Yöntemi İle Ülkelerin Kümelenmesi. Alphanumeric Journal, 8(1), 111-130. https://doi.org/10.17093/alphanumeric.588835

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