Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined with Nature-Inspired Optimization on Microarray Dataset

Abstract

Leukemia is the formation of cancer with different characteristic findings. According to the progress type of disease in the body is called acute or chronic. Acute leukemias are characterized by the presence of blast cells that proliferate uncontrollably in the bone marrow and then go into the blood and tissues. Determination of T/B or non T/B cell class is important in the immunophenotypic evaluation related to subtypes of blast cells. Because the diagnosis and treatment processes of B-ALL, T-ALL and T-LL subtypes, which are composed of B and T cell lines, are different. Therefore, correct diagnosis is vital. In this study, the molecular diagnosis was provided for the accurate detection of T-ALL, B-ALL and T-LL subtypes through microarray datasets. But, microarray datasets have a multidimensional structure. Because it contains information related to the disease as well as information not related to the disease. This situation also affects the training situation and computational cost of the model. For this, the whale optimization algorithm was used in the first stage of the study. Thus, related genes were selected from the data set. Secondly, the selected potential genes were given as input to the ANFIS structure. Then, in order to improve the inference power, parameter optimization related to the membership function of the ANFIS structure was provided with ABC and PSO optimization algorithms. Finally, the predictions obtained from the ANFIS, ANFIS+ABC, and ANFIS+PSO methods for each sample were classified using the logistic regression algorithm and, an accuracy rate of 86.6% was obtained.

Keywords

• Microarray Dataset
• Metaheuristic Optimization Algorithms
• Adaptive Network-Based Fuzzy Inference System
• Logistic Regression

Mikrodizi Veri Kümesi Üzerinde Doğadan İlham Alan Optimizasyon ile Birleştirilen Uyarlanabilir Ağ Tabanlı Bulanık Çıkarım Sistemi Kullanılarak T-ALL, B-ALL ve T-LL Malignitelerinin Sınıflandırılması

Öz

Lösemi farklı karakteristik bulgular gösteren kanser oluşumudur. Hastalığın vücut içerisinde ilerleme biçimine göre akut ya da kronik olarak isimlendirilir. Akut lösemiler, kemik iliğinde kontrolsüz çoğalan ve ardından kana ve dokulara geçen blast hücrelerinin varlığı ile karakterize edilir. Blast hücrelerinin alt türlerine ilişkin immünfenotipik değerlendirme sürecinde T/B ya da non T/B hücre sınıfının belirlenmesi önemlidir. Çünkü, B ve T hücre serisinden meydana gelen B-ALL, T-ALL ve T-LL alt türlerinin teşhis ve tedavi süreçleri farklıdır. Bu nedenle doğru tanı hayatidir. Bu çalışmada, mikrodizi veri kümeleri vasıtasıyla T-ALL, B-ALL ve T-LL alt türlerinin doğru tespiti için moleküler tanı sağlanmıştır. Fakat mikrodizi veri kümeleri, çok boyutlu bir yapıya sahiptir. Çünkü hastalıkla ilişkili bilgilerin yanı sıra hastalıkla ilişkisiz bilgiler de barındırmaktadır. Bu durum modelin eğitim durumunu ve hesaplama maliyetini de etkilemektedir. Bunun için çalışmanın ilk aşamasında balina optimizasyon algoritması kullanılmıştır. Böylece ilişkili genler veri setinden seçilmiştir. İkinci olarak seçilen potansiyel genler ANFIS yapısına girdi olarak verilmiştir. Ardından çıkarım gücünü iyileştirmek için ABC ve PSO optimizasyon algoritmaları ile ANFIS yapısının üyelik fonksiyonuna ilişkin parametre optimizasyonu sağlanmıştır. Son olarak her bir örnek için ANFIS, ANFIS+ABC, ANFIS+PSO yöntemlerinden elde edilen tahminler, lojistik regresyon algoritması kullanılarak sınıflandırılmış ve %86,6 doğruluk oranı elde edilmiştir.

Anahtar Kelimeler

• Mikrodizi Veri Kümesi
• Metasezgisel Optimizasyon Algoritmaları
• Uyarlanabilir Ağ Tabanlı Bulanık Çıkarım Sistemi
• Lojistik Regresyon

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