Diyabet Verilerinin Permütasyon Önem Özelliği Temelli Derin Öğrenme Yöntemleriyle İncelenmesi

Diyabet, vücuttaki yüksek kan şekeri seviyesi nedeniyle meydana gelen metabolik bir hastalıktır. Tedavi edilmediği takdirde, vücudun birçok hayati organında diyabete bağlı sağlık sorunları meydana gelebilir. Makine öğrenme teknolojilerindeki son teknikler ile diyabet hastalığını erken bir aşamada teşhis edebilen uygulamalar kullanılabilir. Bu çalışmada Medical City Hastanesi Endokrinoloji ve Diyabet Uzmanlık Merkezi Al Kindy Eğitim Hastanesi laboratuvarlarından elde edilen veri seti kullanılmıştır. Veri seti, Normal, pre diyabet ve diyabet şeklinde 3 farklı sınıftan oluşmaktadır. Elde edilen diyabet veri seti Uzun-Kısa Vadeli Bellek (LSTM), Evrişimsel Sinir Ağları (CNN) ve Geçitli Tekrarlayan Birim (GRU) derin öğrenme yöntemleri kullanılarak sınıflandırılmıştır. Her algoritmanın sınıflandırma başarımı; doğruluk, kesinlik, duyarlılık ve F skor başarım parametreleri ile değerlendirilmiştir. Derin öğrenme yöntemlerinden, LSTM algoritmasıyla %96.5, CNN algoritmasıyla % 94 ve GRU algoritmasıyla %93 sınıflandırma doğruluğu elde edilmiştir. Bu çalışmada ayrıca veri setindeki özelliklerin sınıflandırma başarımına etkisini belirlemek için Permütasyon önem özelliği yöntemi de kullanılmıştır. Bu yöntem ile HbA1c özelliğinin kullanılan derin öğrenme yöntemlerinde önemli bir parametre olduğu ortaya konulmuştur. Gerek LSTM algoritması ile elde edilen sonuçlar, gerekse sınıflandırma başarısına etki eden en önemli özelliğin tespiti çalışmanın özgünlüğünü ortaya koymaktadır. Elde edilen sonuçların sağlık çalışanlarına hastalığın erken tespitine yardımcı olabilecek etkin karar verme için prognostik araç sağlayacağını göstermektedir.

Anahtar Kelimeler:

Diyabet, Sınıflandırma, Derin öğrenme, Permütasyon önem özelliği

Investigation Of Diabetes Data with Permutation Feature Importance Based Deep Learning Methods

Diabetes is a metabolic disease that occurs due to high blood sugar levels in the body. If it is not treated, diabetes-related health problems may occur in many vital organs of the body. With the latest techniques in machine learning technologies, some of the applications can be used to diagnose diabetes at an early stage. In this study, the data set from the laboratories of Medical City Hospital Endocrinology and Diabetes Specialization Center Al Kindy Training Hospital was used. The dataset consists of 3 different classes: normal, pre-diabetes and diabetes. The obtained diabetes dataset was classified using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) deep learning methods. The classification performance of each algorithm was evaluated with accuracy, precision, sensitivity and F score performance parameters. Among the deep learning methods, 96.5% classification accuracy was obtained with the LSTM algorithm, 94% with the CNN algorithm and 93% with the GRU algorithm. In this study, the Permutation Feature Importance (PFI) method was also used to determine the effect of features in the data set on classification performance. With this method, study reveals that the HbA1c feature is an important parameter in the used deep learning methods. Both the results obtained with the LSTM algorithm and the determination of the most important feature affecting the classification success reveal the originality of the study. It shows that the obtained results will provide healthcare professionals with a prognostic tool for effective decision-making that can assist in the early detection of the disease.

Keywords:

Diabetes, Classification, Deep learning, Permutation importance feature.,

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