Asya Ülkelerinin Beklenen Yaşam Süresi Bakımından Sınıflandırılmasında Etkili Olan Sosyoekonomik Değişkenlerin Kısmi En Küçük Kareler Diskriminant Analizi ile Belirlenmesi

Her ne kadar son yıllarda yapılan çalışmalar doğumda yaşam beklentisinin (DYB) hemen hemen tüm toplumlardaartmakta olduğunu göstermiş olsa da, ölümlerde ve dolayısıyla DYB'de toplum içinde olduğu gibi toplumlar arasındada önemli farklılıklar vardır. DYB'deki bu eşitsizliğin köklerinin, farklı sosyal grupların farklı sosyoekonomik arkaplanlarında olduğuna inanılmaktadır. Yaşam beklentisi, en önemli sağlık sonuçlardan biri ve insani gelişmişliğinönemli bir göstergesi olarak kabul edilmektedir. Bu çalışmanın amacı, bir kaç sosyoekonomik değişkenin beklenenyaşam süresi ile ilişkisini araştırmak ve Asya’daki 51 ülke için DYB’ye etkili olan en önemli değişkenleribelirlemektir. Bu amaçla, Dünya Nüfus Veri Sayfası, 2015’den elde edilen veri kümesi üzerinde Kısmi En KüçükKareler Diskriminant Analizi uygulanmıştır. Analiz sonucunda Asya ülkelerinin beklenen yaşam sürelerine göre Asyaortalamasının üstünde ve altında olmak üzere iki sınıf olarak sınıflandırılmasında en önemli/etkili değişkenin BebekÖlüm Oranı olduğu görülmüştür.

Determination of Socioeconomic Variables Affecting the Classification of Asian Countries in Terms of Life Expectancy by Partial Least Squares Discriminant Analysis

Although studies conducted in recent years have shown that life expectancy at birth (LEB) is increasing in almost all societies, there are significant differences between deaths and, therefore, in LEB, occur among societies as well as within society. It is believed that this inequality in the LEB have its roots in different socioeconomic backgrounds of different social groups. Life expectancy is recognized as one of the most important health outcomes and an important indicator of human development. The aim of this study is to investigate the relationship between several socioeconomic variables and life expectancy and to identify the most important variables that affect the LEB for 51 countries in Asia. For this purpose, Partial Least Squares Discriminant Analysis was applied on the data set obtained from the World Population Data Sheet, 2015. As a result of the analysis, it was found that the most important/effective variable in the classification of Asian countries into two classes as above and below the Asian average according to their life expectancy, is the Infant Mortality Rate.

___

  • [1] Ali M., Ali M. 2015. Discriminant Analysis of Socioeconomic Factors of Life Expectancy at Birth in Asia. Science International (Lahore), 27 (5): 3971-3975.
  • [2] Dowd K., Blake D., Cairns A.J.G. 2010. Facing up to Uncertain Life Expectancy: The Longevity Fan Charts, Demography, 47(1): 67-78.
  • [3] Wagstaff A. 2000. Socioeconomic Inequalities in Child Mortality: Comparisons Across Nine Developing Countries. Bulletin of the World Health Organization, 78: 19-29.
  • [4] Sastry N. 2004. Trends in Socioeconomic Inequalities in Mortality in Developing Countries: The Case of Child Survival in Sao Paulo, Brazil, Demography, 41(3): 443-464.
  • [5] Sufian A.J.M. 2013. Life Expectancy and Its Socioeconomic Determinants-A Discriminant Analysis of National Level Data. International Journal of Humanities and Social Science, Special Issue 3 (12): 303-312.
  • [6] Polat E. 2009. Kısmi En Küçük Kareler Regresyonu. Yüksek Lisans Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, 170s, Ankara.
  • [7] Kemalbay G., Korkmazoglu Ö.B. 2012. Econometrics Application of Partial Least Squares Regression: An Endogeneous Growth Model for Turkey. Procedia-Social and Behavioral Sciences, 62: 906-910.
  • [8] Kemalbay G., Korkmazoglu Ö.B. 2012. Effects of Multicollinearity on Electricity Consumption Forecasting using Partial Least Squares Regression. Procedia-Social and Behavioral Sciences, 62: 1150-1154.
  • [9] Ümit A.Ö., Bulut E. 2013. Türkiye’de İşsizliği Etkileyen Faktörlerin Kısmi En Küçük Kareler Regresyon Yöntemi ile Analizi: 2005-2010 Dönemi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 37: 131-142.
  • [10] Serrano-Cinca C., Gutiérrez-Nieto B. 2013. Partial Least Square Discriminant Analysis for Bankruptcy Prediction. Decision Support Systems, 54: 1245-1255.
  • [11] Sawatsky M.L., Clyde M., Mee F. 2015. Partial Least Squares Regression in the Social Sciences. The Quantitative Methods for Psychology, 11 (2): 52-62.
  • [12] Sghaier A., Jabeur S.B., Bannour B. 2018. Using Partial Least Square Discriminant Analysis to Distinguish Between Islamic and Conventional Banks in the MENA Region. Rev Financ Econ., 36: 133-148.
  • [13] Yoon J., Klasen S. 2018. An Application of Partial Least Squares to the Construction of the Social Institutions and Gender Index (SIGI) and the Corruption Perception Index (CPI). Soc Indic Res, 138: 61-88.
  • [14] Polat E. 2018. Determination of the Effective Economic and/or Demographic Indicators in Classification of European Union Member and Candidate Countries Using Partial Least Squares Discriminant Analysis. Journal of Data Science, 16 (1): 79-92.
  • [15] Fordellone M., Bellincontro A., Mencarelli F. 2018. Partial Least Squares Discriminant Analysis: A Dimensionality Reduction Method to Classify Hyperspectral Data. https://arxiv.org/pdf/1806.09347.pdf (Erişim Tarihi: 07.10.2019)
  • [16] Wiklund S., Nilsson D., Eriksson L., Sjöström M., Wold S., Faber K. 2007. A Randomization Test for PLS Component Selection. Journal of Chemometrics, 21: 427-439.
  • [17] Pérez-Enciso M., Tenenhaus M. 2003. Prediction of Clinical Outcome with Microarray Data: A Partial Least Squares Discriminant Analysis (PLS-DA) Approach. Human Genetics, 112: 581-592.
  • [18] Polat E., Gunay S. 2009. Kısmi En Küçük Kareler ve Bir Uygulama. VI. İstatistik Günleri Sempozyumu Bildiriler Kitabı, 437-444.
  • [19] Tenenhaus M. 1998. La Régression PLS Théorie et Pratique. Editions Technip. 27, Rue Ginoux 75737, Paris Cedex 1.
  • [20] Goyal M.K., Ojha C.S.P. 2010. Application of PLS-Regression as Downscaling Tool for Pichola Lake Basin in India. International Journal of Geosciences, 1: 51-57.
  • [21] Ibrahim M.A.M. 2009. Comparison Between Different Procedures to Determine the Relative Importance of the Lifetime Performance Traits in Predicting Breeding Values of Holstein Cows. Egyptian Journal of Animal Production, 46 (2): 93-102.
  • [22] Rohman A., Lumakso F.A., Riyanto S. 2016. Use of Partial Least Square Discriminant Analysis Combined with Mid Infrared Spectroscopy for Avocado Oil Authentication. Research Journal of Medicinal Plants, 10 (2): 175-180.
  • [23] Almeida M.R., Correa D.N., Rocha W.F.C., Scafi F.J.O. 2013. Discrimination Between Authentic and Counterfeit Banknotes Using Ramanspectroscopy and PLS-DA with Uncertainty Estimation. Microchemical Journal, 109: 170-177.
  • [24] Ruiz-Perez D., Narasimha G. 2018. So you think you can PLS-DA? IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 18-20 October, Las Vegas, NV, USA. https://www.biorxiv.org/content/biorxiv/early/2018/01/15/207225.full.pdf (Erişim Tarihi: 07.10.2019)
  • [25] Barker M., Rayens W.S. 2003. Partial Least Squares for Discrimination. Journal of Chemometrics, 17: 166-173.
  • [26] Brereton R.G., Lloyd G.R. 2014. Partial Least Squares Discriminant Analysis: Taking the Magic Away. Journal at Chemometrics, 28: 213-225.
  • [27] Partial Least Squares Discriminant Analysis PLSDA Tutorial. 2018. https://help.xlstat.com/customer/en/portal/articles/2062368-partial-least-squares-discriminantanalysis-plsda-tutorial?b_id=9283 (Erişim Tarihi: 18.09.2019)
  • [28] Xlstat, 2018. Paris, France. https://help.xlstat.com/customer/en/portal/articles/2178395-download-thexlstat-help-documentation (Erişim Tarihi: 18.09.2019)
  • [29] Polat E., Gunay S. 2015. The Comparison of Partial Least Squares Regression, Principal Component Regression and Ridge Regression with Multiple Linear Regression for Predicting PM10 Concentration Level Based on Meteorological Parameters. Journal of Data Science, 13 (2): 663-692.
  • [30] Rosipal R., Krämer N. 2006. Overview and Recent Advances in Partial Least Squares, in Subspace, Latent Structure and Feature Selection. Edited by Saunders C., Grobelnik M., Gunn S. & Taylor J.S., Springer: Berlin, 34-51.
  • [31] Wold S., Sjöström M., Eriksson L. 2001. PLS-Regression: A Basic Tool of Chemometrics. Chemometrics and Intelligent Laboratory Systems, 58: 109-130.
  • [32] Sjöström M., Wold S., Söderström B. 1986. PLS Discriminant Plots, in Pattern Recognition in Practice. Ed: Gelsema E.S., Kanal L.N. (Amsterdam: Elsevier), 461-470.
  • [33] Ballabio D., Consonni V. 2013. Classification Tools in Chemistry. Part 1: Linear Models. PLS-DA, Analytical Methods, 5: 3790-3798.
  • [34] Aliakbarzadeh G., Parastar H., Sereshti H. 2016. Classification of Gas Chromatographic Fingerprints of Saffron Using Partial Least Squares Discriminant Analysis Together with Different Variable Selection Methods. Chemometrics and Intelligent Laboratory Systems, 158: 165-173.
Bitlis Eren Üniversitesi Fen Bilimleri Dergisi-Cover
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 2012
  • Yayıncı: Bitlis Eren Üniversitesi Rektörlüğü