TIBBİ VERİ KÜMELERİNDE GENETİK ALGORİTMALARLA ÖZELLİK SEÇİMİ VE SINIFLANDIRMA BAŞARIMINA ETKİSİ

Günümüzde çok büyük boyuttaki tıbbi veri tabanlarından, klinik karar destek sistemlerinin faydalı bilgiler elde etmesi oldukça zorlaşmıştır. Genetik algoritmalar (GA) yaygın olarak kullanılan bir özellik seçme yöntemidir ve en iyi çözümleri verebilir. Bu çalışmada, çok sayıda karmaşık verilere sahip olan tıbbi verilerden özellik seçimi yapmak ve en uygun özellik alt kümesini oluşturarak sınıflandırma başarısını artırmak için GA içeren bir model önerilmiştir. Önerilen yöntemin performansını değerlendirmek için çalışmada en çok bilinen ve rahatlıkla ulaşılabilen 5 tıbbi veri kümesi ve 7 farklı denetimli sınıflandırma yöntemi kullanılmıştır. Her veri kümesi ile her sınıflandırıcı için ayrı ayrı özellik seçimi ve sınıflandırma uygulamaları yapılmıştır. Bu uygulamalarda elde edilen sonuçlar, önerilen yaklaşımla yapılan sınıflandırmalarda, veri kümesine bağlı olarak, Doğruluk oranında dolayısıyla makine öğrenmesi modeli performansında ortalama %2 ile %21 arasında artış sağlandığını ortaya koymuştur. Ayrıca yapılan çalışmalarda denetimli sınıflandırma algoritmalarından Rastgele Ormanın bütün veri kümelerinde diğer algoritmalardan daha iyi sonuçlar verdiği görülmekte ve tıbbi veri kümelerindeki sınıflandırma başarısı ile öne çıktığı görülmüştür.

Anahtar Kelimeler:

Özellik Seçimi, Genetik Algoritmalar, Tıbbi Veri Kümeleri, Özellikler Alt Kümesi, Tıbbi Sınıflandırma

FEATURE SELECTION WITH GENETIC ALGORITHMS AND ITS EFFECT ON CLASSIFICATION PERFORMANCE IN MEDICAL DATASETS

Nowadays, it has become very difficult for clinical decision support systems to obtain useful information from very large medical databases. Genetic algorithms (GA) are a widely used feature selection method and can give the best solutions. In this study, a model with GA is proposed to select features from medical data with a large number of complex data and to increase classification success by creating the most appropriate feature subset. In order to evaluate the performance of the proposed method, 5 most well-known and easily accessible medical data sets and 7 different supervised classification methods were used in the study. Feature selection and classification applications were made separately for each data set and each classifier. The results obtained in these applications revealed that, depending on the data set, in the classifications made with the proposed approach, an average of 2% to 21% increase was achieved in the accuracy rate and thus in the machine learning model performance. In addition, it is seen that the Random Forest, one of the supervised classification algorithms, gives better results in all data sets than other algorithms and it has been seen that it stands out with its classification success in medical datasets.

Keywords:

Future Selection, Genetic Algorithms, Medical Data Set, Futures Subset, Medical Classification,

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