Makine Öğrenmesi Yöntemleri ile Böbrek Yetmezliği Hastalığını Etkileyen Faktörlerin Sınıflandırılması

Makine öğrenmesi yöntemleri, sağlık araştırmalarında veri analizi için yaygın olarak kullanılmaktadır. Bu çalışmanın amacı, Yapay Sinir Ağları (Çok Katmanlı Algılayıcı), Destek Vektör Makineleri, Naive Bayes, Karar Ağaçları, Rastgele Orman Algoritması, K-En Yakın Komşu Algoritması gibi çeşitli makine öğrenmesi yöntemlerini kullanarak böbrek yetmezliğini etkileyen faktörleri sınıflandırmaktır. Bu çalışmada, Ankara Numune Hastanesi’nde acil servise gelen, 18 yaşından büyük ve üst gastrointestinal kanama belirtileri bulunan 237 hasta seçilmiştir. Burada makine öğrenmesi yöntemleri ile sınıflandırma yapmak için böbrek yetmezliğini etkileyen yaş, cinsiyet, kan değerleri, diğer hastalıklar vb. gibi 34 değişken kullanılmıştır. Makine öğrenmesi yöntemleri doğruluk oranları, tahmin, duyarlılık, özgüllük ve Kappa değerlerine göre karşılaştırıldığında, karar ağaçları algoritmasının iyi performans gösterdiği bulunmuştur.

Anahtar Kelimeler:

Makine Öğrenmesi, Sınıflandırma, Karar Ağaçları, Böbrek Yetmezliği

Classification of Factors Affecting Renal Failure by Machine Learning Methods

Machine learning methods are widely used for data analysis in health research. The aim of this study is to classify the factors that affect renal failure by using various machine learning methods such as Artificial Neural Networks (Multilayer Perceptron), Support Vector Machines, Naive Bayes, Decision Trees, Random Forests, K-Nearest Neighborhood algorithms. In this study, 237 patients who have been in emergency unit in Hospital of Numune in Ankara and were older than 18 years and have upper gastrointestinal bleeding symptoms have been selected. Here, 34 variables such as age, gender, blood values, other diseases etc. which affect renal failure have been used to make classification with machine learning methods. When machine learning methods are compared according to the accuracy rates, precision, sensivity, specifity and Kappa values, it has been found that decision trees algorithm performs well.

Keywords:

Machine Learning, Classification, Decision Trees, Renal Failure,

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