Burçin Şeyda ÇORBA ZORLU
(Ondokuz Mayıs Üniversitesi, Fen Edebiyat Fakültesi, İstatistik Bölümü, Samsun, Türkiye)
Pelin KASAP
(Ondokuz Mayıs Üniversitesi, Fen Edebiyat Fakültesi, İstatistik Bölümü, Samsun, Türkiye)
Yıl: 2020Cilt: 36Sayı: 1ISSN: 1012-2354Sayfa Aralığı: 89 - 102İngilizce

142 0
Classification of Factors Affecting Renal Failure by Machine Learning Methods
Machine learning methods are widely used for data analysis inhealth research. The aim of this study is to classify the factors that affect renalfailure by using various machine learning methods such as Artificial NeuralNetworks (Multilayer Perceptron), Support Vector Machines, Naive Bayes,Decision Trees, Random Forests, K-Nearest Neighborhood algorithms. In thisstudy, 237 patients who have been in emergency unit in Hospital of Numunein Ankara and were older than 18 years and have upper gastrointestinalbleeding symptoms have been selected. Here, 34 variables such as age,gender, blood values, other diseases etc. which affect renal failure have beenused to make classification with machine learning methods. When machinelearning methods are compared according to the accuracy rates, F-measure,sensivity, specifity and Kappa values, it has been found that decision treesalgorithm performs well.
DergiAraştırma MakalesiErişime Açık
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