Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Hasta Bazlı Heterojenitenin Giderilmesi: Kümeleme Analizi Yaklaşımı: Metodolojik Araştırma
Elimination of Patient-Based Heterogeneity: Cluster Analysis Approach: Methodological Research
Batuhan BAKIRARARa
aAnkara Üniversitesi Tıp Fakültesi, Biyoistatistik ABD, Ankara, Türkiye
Turkiye Klinikleri J Biostat. 2023;15(2):134-40
doi: 10.5336/biostatic.2023-96566
Article Language: TR
Full Text
ÖZET
 Amaç: Çalışmanın amacı, uygun kümeleme yöntemleri ve küme sayıları kullanarak verileri uygun alt gruplara bölmek, ilgilenilen bağımlı değişken için analizleri bu alt gruplarda yaparak sınıflama performansındaki değişimleri incelemektir. Gereç ve Yöntemler: Çalışmada kullanılan veri seti Kaggle veri tabanından alınmıştır. Veri seti, 15 bağımsız ve 1 bağımlı değişken olmak üzere toplam 16 değişkenden oluşmakta olup, 948 hasta verisi içermektedir. Analizler R (ver. 4.1.2) programla dili kullanılarak yapılmış ve bu programla dili içindeki chisquare, wilcox.test, cluster, RWeka ve e1071 paketlerinden faydalanılmıştır. Kümeleme yöntemi olarak K-ortalama, sınıflandırma yöntemleri olarak ise lojistik regresyon, çok katmanlı algılayıcı, karar tablosu, karar destek makinesi, rastgele orman, J48, IBk, torbalama ve oylanmış algılayıcı kullanılmıştır. Veri seti 10 kat çapraz doğrulama kullanılarak test edilmiş ve tüm analizler 1.000 kez tekrarlanarak sonuçlar verilmiştir. Performans kriteri olarak doğru sınıflama oranı, F-ölçütü, Matthews korelasyon katsayısı, ROC eğrisi altında kalan alan ve precision-recall alanı kullanılmıştır. Bulgular: Modeller ile Tip 2 diyabet olacak ve olmayacak hastalar için tahmin sonuçlarına bakıldığında, birinci kümede Tip 2 diyabet görülecek tahmini yapılan hastalara ait performansta genel modele göre artış gözlenmiştir. İkinci kümede Tip 2 diyabet görülmeyecek tahmini yapılan hastalara ait performansta da genel modele göre artış gözlenmiştir. Sonuç: Çalışmada yapılan kümeleme analizi sonrasında hastalara ait spesifik özelliklerin daha iyi belirlendiği ve küme bazlı sınıflandırma ile de oluşturulan modellerinin daha iyi sınıflama performansına sahip olduğu gözlenmiştir.

Anahtar Kelimeler: Kümeleme; K-ortalama; sınıflandırma; makine öğrenmesi; heterojenite
ABSTRACT
 Objective: The aim of the study is to divide the data into appropriate subgroups using appropriate clustering methods and cluster numbers, and to analyze the changes in classification performance by performing the analyzes for the dependent variable of interest in these subgroups. Material and Methods: The data set used in the study was taken from the Kaggle database. The data set consists of 16 variables, 15 independent and 1 dependent variable, and includes 948 patient data. For the analysis, the R (ver. 4.1.2) programming language was used and the chisquare, wilcox.test, cluster, RWeka and e1071 packages in this program were used. Kmeans was used as the clustering method, and logistic regression, multilayer perceptron, decision table, decision support machine, random forest, J48, IBk, bagging and voted perceptron were used as classification methods. The dataset was tested using 10-fold cross validation option and all analyzes were repeated 1,000 time. Accuracy, F-measure, Matthews correlation coefficient, area under ROC curve and precision-recall area were used as performance criteria. Results: Considering the estimation results for patients with and without Type 2 diabetes with the models, an increase was observed in the performance of patients who were predicted to have Type 2 diabetes in the first cluster compared to the general model. An increase was observed in the performance of patients who were predicted not to have Type 2 diabetes in the second cluster compared to the general model. Conclusion: After clustering, it was observed that the specific characteristics of the patients were determined better and the models created with the cluster-based classification had better performance.

Keywords: Clustering; K-means; classification; machine learning; heterogeneity
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