Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
TwoClsBalancer: An Interactive Web Application for Handling the Class Imbalance Problem Based on Machine Learning
TwoClsBalancer: Sınıf Dengesizliği Problemi İçin Makine Öğrenmesine Dayalı Etkileşimli Bir Web Uygulaması
Ahmet Kadir ARSLANa, Cemil ÇOLAKa, Mehmet Cengiz ÇOLAKb
aDepartment of Biostatistics and Medical Informatics, İnönü University Faculty of Medicine, Malatya, Türkiye
bDepartment of Cardiovascular Surgery, İnönü University Faculty of Medicine, Malatya, Türkiye
Turkiye Klinikleri J Biostat. 2022;14(3):168-79
doi: 10.5336/biostatic.2022-88932
Article Language: EN
Full Text
ABSTRACT
 Objective: The main purpose of this research is to develop a novel user-friendly web tool based on machine learning approaches, which applies a variety of techniques to address the class imbalance problem. Material and Methods: Shiny, an opensource R package, was used to develop the proposed web tool. The interactive tool can handle the class imbalance problem for binary classification dataset(s) by implementing sampling-based methods. As a clinical application, the dataset retrospectively obtained from the database of the Cardiovascular Surgery Department of Turgut Özal Medical Center, İnönü University, Malatya, Türkiye was used in this web-based software. To overcome the class imbalance problem, sampling-based methods were implemented on the original dataset. After this process, the classification of hypertension in patients with coronary artery disease was achieved by three classification models. Results: According to the outputs of the developed web application, the best classification performance was obtained by the support vector machines with radial basis function kernel (SVM-RBF) model after applying the density-based synthetic minority over-sampling technique oversampling method. The accuracy, sensitivity, specificity, precision, f-measure, and g-mean metrics of the relevant model were calculated as 0.99, 0.99, 0.99, 0.95, 0.97, and 0.97, respectively. Conclusion: The oversampling methods used in this research indicated a more positive contribution to the classification performance of the models as compared to the undersampling methods. When the undersampling methods were applied, the three classification models did not demonstrate successful classification performance, whereas the SVM-RBF model outperformed the other two models when the oversampling methods were implemented. The designed interactive web application is freely accessible through http://biostatapps.inonu.edu.tr/twoclsbalancer.

Keywords: Classification; coronary artery disease; hypertension; class imbalance problem; web-based application
ÖZET
 Amaç: Bu araştırmanın temel amacı, sınıf dengesizliği sorununu çözmek için çeşitli teknikler uygulayan makine öğrenimi yaklaşımlarına dayalı yeni, kullanıcı dostu bir web aracı geliştirmektir. Gereç ve Yöntemler: Açık kaynaklı bir R paketi olan Shiny, önerilen web aracını geliştirmek için kullanıldı. Etkileşimli araç, örneklemeye dayalı yöntemler uygulayarak ikili sınıflandırma veri kümeleri için sınıf dengesizliği sorununu çözebilir. Web tabanlı bu yazılımda, klinik uygulama olarak Malatya İnönü Üniversitesi Turgut Özal Tıp Merkezi Kalp Damar Cerrahisi Anabilim Dalı veri tabanından geriye dönük olarak elde edilen veri seti kullanılmıştır. Sınıf dengesizliği sorununun üstesinden gelmek için orijinal veri seti üzerinde örneklemeye dayalı yöntemler uygulanmıştır. Bu işlemden sonra koroner arter hastalığı olan hastalarda hipertansiyonun sınıflandırılması üç sınıflandırma modeli ile sağlanmıştır. Bulgular: Geliştirilen web uygulamasının çıktılarına göre en iyi sınıflandırma performansı, 'density-based synthetic minority oversampling technique' aşırı örnekleme yöntemi uygulandıktan sonra radyal tabanlı destek vektör makineleri [support vector machines with radial basis function (SVM-RBF)] modeli ile elde edilmiştir. İlgili modelin doğruluk, duyarlılık, özgüllük, kesinlik, f-ölçümü ve g-ortalama metrikleri sırasıyla 0,99, 0,99, 0,99, 0,95, 0,97 ve 0,97 olarak hesaplanmıştır. Sonuç: Bu araştırmada kullanılan aşırı örnekleme yöntemleri, alt örnekleme yöntemlerine kıyasla modellerin sınıflandırma performansına daha olumlu katkı sağlamıştır. Alt örnekleme yöntemleri uygulandığında, 3 sınıflandırma modeli başarılı sınıflandırma performansı göstermezken, aşırı örnekleme yöntemleri uygulandığında SVM-RBF modeli diğer 2 modelden daha iyi performans göstermiştir. Tasarlanan interaktif web uygulamasına http://biostatapps.inonu.edu.tr/twoclsbalancer adresinden ücretsiz olarak erişilebilir.

Anahtar Kelimeler: Sınıflandırma; koroner arter hastalığı; hipertansiyon; sınıf dengesizliği sorunu; web tabanlı uygulama
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