Classifying OECD Countries According to Health Indicators Using Fuzzy Clustering Analysis

Alptekin, Nesrin; Yeşilaydın, Gözde

doi:10.20491/isader.2015415530

Cited by 9 publications

(4 citation statements)

References 1 publication

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“…Avrupa Birliği ülkelerine göre Türkiye'nin konumunun değerlendirildiği farklı bir araştırmada ise Türkiye, Slovakya, Macaristan ve Çek Cumhuriyeti dışındaki diğer ülkelerin sağlık harcamalarının gayri safi yurt içi hasıla içerisindeki payı bakımından farklılık gösterdikleri belirlenmiştir (Sığırlı vd., 2006: 81-85). Alptekin (2014) çalışmasında ise Türkiye, Bulgaristan, Kıbrıs, Estonya, Macaristan, Letonya, Litvanya, Polonya, Romanya ve Slovakya ile aynı kümede yer almıştır. COVID-19 salgının sağlık ve finans göstergelerindeki etkisinin değerlendirildiği kümeleme analizinde Avrupa ve Amerika kıtalarındaki en yüksek vaka sayılarına sahip ülkeler aynı kümede yer alırken Türkiye ise bir milyon kişi başına vaka ve ölüm oranlarının en düşük olduğu ülkelerle birlikte aynı kümede yer almıştır (Tekin, 2020: 336-349).…”

Section: Kümeleme Anali̇zi̇unclassified

Classification of Statistical Regions by Clustering Analysis According to Health Indicators in Turkey

Köse

2022

Alanya Akademik Bakış

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Bu çalışmanın amacı 2019 yılı sağlık hizmeti talep, üretim ve kapasite boyutlarındaki verilere göre istatistik bölgelerin hiyerarşik kümeleme analizi ile değerlendirilmesidir. 2019 sağlık istatistiği yıllığındaki bu boyutlardaki veriler farklı ölçüm oranlarına sahip olduğundan standartlaştırılmış Z değerleri alınmış ve analizler SPSS for Windows 15.0 programı ile yapılmıştır. Hiyerarşik kümeleme analizinde aglomeratif hiyerarşik kümeleme ile Ward's yöntemi seçilmiştir. Araştırmada 12 istatistik bölgeye ait veriler 3 boyutta ayrı ayrı ve birlikte değerlendirilmiştir. Her bir değerlendirmeye ait aglomeratif tablo ve ağaç diyagramına göre küme grupları belirlenmiştir. Sağlık hizmeti talep, üretim ve kapasite boyutlarında ayrı ayrı ve birlikte değerlendirme aşamalarında istatistik bölgeler 3 kümede gruplanmıştır. İstatistik bölgelerin kümelenmesinde coğrafi yakınlık, sosyoekonomik gelişmişlik düzeyi, nüfus yoğunluğu parametreleri belirleyici role sahiptir.

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Section: Kümeleme Anali̇zi̇unclassified

Classification of Statistical Regions by Clustering Analysis According to Health Indicators in Turkey

Köse

2022

Alanya Akademik Bakış

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“…Bulğurcu (2014) estimated the unemployment rate for 2013 with the fuzzy inference system (ANFIS) based on adaptive network structure using the unemployment rates between 2000 and 2012. Alptekin and Yeşilaydın (2015) categorized Organization for Economic Co-operation and Development (OECD) countries by fuzzy clustering analysis in terms of health variables.…”

Section: Ii-literature Reviewmentioning

confidence: 99%

Bulanık Kural Tabanı ile Genel Sağlık Sigortası Primlerinin Hesaplanması

Ertuğrul¹,

Öztaş²

2018

Sosyal Güvenlik Dergisi

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As of 2008, under the name of Social Security Reform, Health Transformation program has been launched in Turkey and nobody has been aimed to remain out of the system. Even though this system provides comprehensive advantages to citizens, it also has some drawbacks about premium calculations. In the current system, premiums are calculated by using fixed ratios for different income levels. However, this causes inequalities among the citizens. Therefore, in this article, the elimination of inequalities and the establishment of fairer premium system that provides an interdisciplinary perspective about the general health insurance was aimed to present. As a methodology fuzzy logic that provides fuzzy grading among individuals was determined. The scope of the application has been limited to individuals who are considered as poor, who have no social security and who are insured on a voluntary basis. Premiums were calculated by fuzzy rules based on expert opinion with the help of MATLAB Fuzzy Logic Designer Toolbox for 4,650 people participating in the TURKSTAT's income survey. Then, total premium will be paid by the number of 9,749,855 people was estimated by weighting the calculated premiums. As a result, the premiums were calculated more fairly and the situations that create inequalities among the citizens were abolished. In addition, besides income and expenses, age was used for equitable calculation in order not to make voluntarily insured be advantageous. When literature was examined, it was thought that this article will contribute to the interdisciplinary literature since fuzzy logic applications are very rare in social sciences.

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“…The subject of classification is very popular in the literature and is one of the most studied topics. Some of the studies are as follows; Alptekin and Yeşilaydın (2015), have worked on the classification of OECD countries according to health indicators by using the fuzzy cluster method. Haltaş and Alkan (2016), worked on automatic classification of cancer types.…”

Section: Introductionmentioning

confidence: 99%

Determination and Classification of Importance of Attributes Used in Diagnosing Pregnant Women's Birth Method

Çelik

2020

Alphanumeric Journal

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The rapid development of information technologies enables successful results in computer-aided studies. This has led researchers to investigate the usability of technologies such as computer and software supported systems, machine learning, and artificial intelligence in many studies. One of these areas is health. For example, in order not to risk the condition of the mother and baby, in some cases, it is very important to correctly determine the times when the cesarean operation, which is mandatory, is mandatory. In this context, in order to make a faster and more accurate decision, it is very important to determine which attributes and how important the level is in making obligatory cesarean. In this study, to determine whether or not caesarean is necessary in the literature, the importance level of the five criteria taken into consideration has been determined and an attribute determination has been carried out and then a classification has been made. The data set used belongs to 80 pregnant women with 6 attributes. Although the same data set was previously classified with different methods, no study was found on determining the significance levels of the attributes and using artificial neural networks as a method. For this reason, in this study, the feature was determined using an adaptive nerve-fuzzy classifier and classified using artificial neural networks. When the results are examined, it is concluded that the importance levels of the attributes are different. Although the values such as accuracy, Sensitivity, and Specificity calculated to evaluate the classification results were found to be quite high for the training set, it was observed that the desired success was not achieved in the test data. While this result is promising, it also reveals the need to increase the learning performed with larger data sets.

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Classifying OECD Countries According to Health Indicators Using Fuzzy Clustering Analysis

Cited by 9 publications

References 1 publication

Classification of Statistical Regions by Clustering Analysis According to Health Indicators in Turkey

Classification of Statistical Regions by Clustering Analysis According to Health Indicators in Turkey

Bulanık Kural Tabanı ile Genel Sağlık Sigortası Primlerinin Hesaplanması

Determination and Classification of Importance of Attributes Used in Diagnosing Pregnant Women's Birth Method

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