Yıl 2018, Cilt 19, Sayı 4, Sayfalar 907 - 925 2018-12-31

Comparison of Provinces of Turkey In Terms of Accessing Health Care Services by Using Different Clustering Algorithms

Hasan YILDIRIM [1]

0 15

Having access to health care services is the most primary of the basic human rights. It is vital that all citizens in a country are able to get these services equally and homogeneously. Despite its importance, providing health services vary from both country to country and between different provinces or states in country. Similarly, it may cause severe disparities and negative effects between individuals in the society. The primary concern of this study is to determine whether there is any difference between Turkey provinces in terms of accessing health care services, or not. Several clustering algorithms including hierarchical clustering, k-means and partitioning around medoids (pam) were applied to the data set including 31 health indicators of all provinces in Turkey. After comparing these algorithms via using some measures for determining the number of clusters and cluster validity, the findings showed that there were four distinct and significant clusters based on k-means clustering algorithm. It seemed that these clustering results were in a a close reciprocal relationship with the economic development and geographical location of provinces. Clustering results were evaluated and interpreted according to these two important findings.
Cluster analysis, health services, hierarchical clustering, k means, cluster validity
  • [1] WHO. Basic Documents, Forty-Third Edition, World Health Organization 2001; apps.who.int/gb/bd/. (Last Access Date: 07/04/2018)
  • [2] Pala K. Türkiye İçin Nasıl Bir Sağlık Reformu? 2005. Bursa Nilüfer Belediyesi, Bursa.
  • [3] TTB. Sağlıkta gündem: Herkese eşit fırsat mı? Serbest piyasa egemenliği mi? Sağlık Bakanlığı Ulusal Sağlık Politikası Taslak Dökümanı Değerlendirme Raporu. Türk Tabipleri Birliği 1992; Ankara.
  • [4] Çınaroğlu S, Avcı K. İstatistiki bölge birimlerinin seçilen sağlık göstergeleri bakımından kümelenmesi. Hacettepe Sağlık İdaresi Dergisi 2014; 17.2.
  • [5] Tekin B. Temel sağlik göstergeleri açısından Türkiye'deki illerin gruplandırılması: Bir kümeleme analizi uygulaması. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2015; 5(2): 389.
  • [6] Taban S. Türkiye'de sağlık ve ekonomik büyüme arasındaki nedensellik ilişkisi. Sosyoekonomi 2006; 4.4.
  • [7] T.C. Sağlık Bakanlığı, Sağlık İstatistikleri Yıllığı 2016; Ankara. https://dosyasb.saglik.gov.tr/Eklenti/13183,sy2016turkcepdf.pdf?0 (Son Erişim Tarihi: 07/04/2018)
  • [8] Hamarat B. Türkiye’de sağlık açısından homojen il gruplarının belirlenmesine ilişkin istatiksel bir yaklaşım. Y. Lisans Tezi, Anadolu Üniversitesi, Eskişehir, 1998.
  • [9] Çilan ÇA, Demirhan, A. Türkiye’nin illere göre sosyoekonomik yapısının çok boyutlu ölçekleme tekniği ve kümeleme analizi ile incelenmesi. Yönetim Dergisi 2002; 42: 39-50.
  • [10] Albayrak AS, Kalaycı Ş, Karataş A. Türkiye'de coğrafi bölgelere göre illerin sosyoekonomik gelişmişlik düzeylerinin temel bileşenler analiziyle incelenmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2004; 9:2.
  • [11] Karabulut M, Gürbüz M, Sandal EK. Hiyerarşik kluster (küme) tekniği kullanılarak Türkiye’de illerin sosyo-ekonomik benzerliklerinin analizi. Coğrafi Bilimler Dergisi 2004; 2.2: 65-78.
  • [12] Kaygısız Z, Saraçlı S, Dokuzlar KU. İllerin gelişmişlik düzeyini etkileyen faktörlerin path analizi ve kümeleme analizi ile incelenmesi. VII. Ulusal Ekonometri ve İstatistik Sempozyumu; 2005. İstanbul Üniversitesi, İstanbul, Türkiye.
  • [13] Filiz Z. İllerin sosyo-ekonomik düzeylerine göre gruplandırılmasında farklı yaklaşımlar. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi 2014; 6.1: 77-100.
  • [14] Altıparmak A. Sosyo-ekonomik göstergeler açısından illerin gelişmişlik düzeyinin karşılaştırmalı analizi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2005; 24.
  • [15] Taner T, Erilli A, Yüksel Ö, Yolcu U. İllerin sosyoekonomik verilere dayanarak bulanık kümeleme analizi ile sınıflandırılması. Physical Sciences 2009; 4(1): 1-11.
  • [16] Yılancı AGV. Bulanık kümeleme analizi ile Türkiye'deki illerin sosyoekonomik açıdan sınıflandırılması. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2010; 15(3).
  • [17] Yıldız EB, Sivri U, Berber M. Türkiye’de illerin sosyo-ekonomik gelişmişlik sıralaması. Uluslararası Bölgesel Kalkınma Sempozyumu; 2010. Bozok Üniversitesi, Yozgat, Türkiye.
  • [18] Çemrek F. Türkiye’deki illerin gelir ve refah düzeyi değişkenleri arasındaki ilişkinin kanonik korelasyon analizi ile incelenmesi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi 2012; 7.2.
  • [19] Çelik Ş. Kümeleme analizi ile sağlık göstergelerine göre Türkiye’deki illerin sınıflandırılması. Doğuş Üniversitesi Dergisi 2013; 14 (2): 175-194
  • [20] Rencher AC. Methods of Multivariate Analysis. (Vol. 492), New York, NY, USA: Wiley, 2002
  • [21] Sharma S. Applied Multivariate Techniques. New York, NY, USA: Wiley, 1995.
  • [22] Yıldırım H. The usage of dissimilarity and similarity measures in statistics. Msc, Çukurova University, Adana, Turkey, 2015.
  • [23] Hartigan J. Clustering algorithms. New York, NY, USA: Wiley, 1975.
  • [24] Yan M. Methods of determining the number of clusters in a data set and an clustering criterion. Ph.D, Institute and State University, Blacksburg, Virginia, 2005.
  • [25] Mo'oamin MR, Hamad BS. Using cluster analysis and discriminant analysis methods in classification with application on standard of living family in palestinian areas. International Journal of Statistics and Applications 2015; 5.5: 213-222.
  • [26] Everitt BS, Landau S, Leese M, Stahl D. Cluster Analysis. 5th Edition. New York, NY, USA: Wiley, 2011.
  • [27] Choi SS, Cha SH, Tappert, CC. A survey of binary similarity and distance measures. Journal of Systemics, Cybernetics and Informatics 2010; 8.1: 43-48.
  • [28] Cha SH. Comprehensive survey on distance/similarity measures between probability density functions. City 2007; 1.2: 1.
  • [29] Lawson RG, Jurs PC. New index for clustering tendency and its application to chemical problems. Journal of Chemical Information and Computer Sciences 1990; 30.1: 36-41.
  • [30] Kassambara A. Practical guide to cluster analysis in R: Unsupervised machine learning. (Vol. 1). Sthda, 2017.
  • [31] Banerjee A, Dave RN. Validating clusters using the Hopkins statistic. In: Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference On. IEEE; 2004; pp. 149-153.
  • [32] Dubes RC, Jain AK. Algorithms for clustering data. Prentice Hall, Englewood Cliffs, 1988
  • [33] Tan PN, Steinbach M, Kumar V. Introduction to data mining. 1st. Boston, Pearson Addison Wesley, 2005.
  • [34] Beglinger LJ, Smith TH. A review of subtyping in autism and proposed dimensional classification model. Journal of Autism and Developmental Disorders 2001; 31:4: 411-422.
  • [35] Sokal RR. A statistical method for evaluating systematic relationship. University of Kansas Science Bulletin 1958; 28: 1409-1438.
  • [36] Charrad M, Ghazzali BV, Niknafs A. NbClust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software 2014; 61(6): 1-36.
  • [37] Sørensen T. A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on danish commons. Biol. Skr 1948; 5:1: 34.
  • [38] Ward JR, Joe H. Hierarchical grouping to optimize an objective function. Journal of The American Statistical Association 1963; 58.301: 236-244.
  • [39] Kaufman L, Rousseeuw PJ. Finding groups in data: An introduction to cluster analysis. New York, NY, USA: Wiley 1990.
  • [40] Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K. Cluster: Cluster analysis basics and extensions. R Package Version 2012; 1.2: 56.
  • [41] Hartigan JA, Wong MA. Algorithm as 136: A k-means clustering algorithm. Journal Of The Royal Statistical Society. Series C (Applied Statistics) 1979; 28.1: 100-108.
  • [42] Ansari Z, Azeeem MF, Ahmet W, Babu AV. Quantitative evaluation of performance and validity indices for clustering the web navigational sessions. CoRR 2015. abs/1507.03340
  • [43] R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Url http://www.r-project.org/.
  • [44] Halkidi M, Vazirgiannis M, Batistakis Y. Quality scheme assessment in the clustering process. In: European Conference On Principles of Data Mining And Knowledge Discovery. Springer, Berlin, Heidelberg 2000; 265-276.
  • [45] Krzanowski WJ, Lai YT. A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics 1988; 23-34.
  • [46] Caliński T, Harabasz, J. A dendrite method for cluster analysis. Communications in Statistics-Theory and Methods 1974; 3.1: 1-27.
  • [47] Davies DL, Bouldin, DW. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1979; 2: 224-227.
  • [48] Brock G, Pihur V, Datta S, Datta S. ClValid: An R package for cluster validation. Journal of Statistical Software 2011.
Birincil Dil en
Konular Mühendislik
Dergi Bölümü Makaleler
Yazarlar

Yazar: Hasan YILDIRIM

Bibtex @araştırma makalesi { estubtda515787, journal = {Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering}, issn = {2667-4211}, address = {Eskişehir Teknik Üniversitesi}, year = {2018}, volume = {19}, pages = {907 - 925}, doi = {10.18038/aubtda.413890}, title = {Comparison of Provinces of Turkey In Terms of Accessing Health Care Services by Using Different Clustering Algorithms}, key = {cite}, author = {YILDIRIM, Hasan} }
APA YILDIRIM, H . (2018). Comparison of Provinces of Turkey In Terms of Accessing Health Care Services by Using Different Clustering Algorithms. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, 19 (4), 907-925. DOI: 10.18038/aubtda.413890
MLA YILDIRIM, H . "Comparison of Provinces of Turkey In Terms of Accessing Health Care Services by Using Different Clustering Algorithms". Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 19 (2018): 907-925 <http://dergipark.gov.tr/estubtda/issue/42733/515787>
Chicago YILDIRIM, H . "Comparison of Provinces of Turkey In Terms of Accessing Health Care Services by Using Different Clustering Algorithms". Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 19 (2018): 907-925
RIS TY - JOUR T1 - Comparison of Provinces of Turkey In Terms of Accessing Health Care Services by Using Different Clustering Algorithms AU - Hasan YILDIRIM Y1 - 2018 PY - 2018 N1 - doi: 10.18038/aubtda.413890 DO - 10.18038/aubtda.413890 T2 - Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering JF - Journal JO - JOR SP - 907 EP - 925 VL - 19 IS - 4 SN - 2667-4211- M3 - doi: 10.18038/aubtda.413890 UR - http://dx.doi.org/10.18038/aubtda.413890 Y2 - 2018 ER -
EndNote %0 Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering Comparison of Provinces of Turkey In Terms of Accessing Health Care Services by Using Different Clustering Algorithms %A Hasan YILDIRIM %T Comparison of Provinces of Turkey In Terms of Accessing Health Care Services by Using Different Clustering Algorithms %D 2018 %J Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering %P 2667-4211- %V 19 %N 4 %R doi: 10.18038/aubtda.413890 %U 10.18038/aubtda.413890
ISNAD YILDIRIM, Hasan . "Comparison of Provinces of Turkey In Terms of Accessing Health Care Services by Using Different Clustering Algorithms". Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 19 / 4 (Aralık 2019): 907-925. http://dx.doi.org/10.18038/aubtda.413890