The Use of Artificial Neural Networks Optimized with Fire Fly Algorithm in Cancer Diagnosis


BİLEN M., IŞIK A. H. , YİĞİT T.

GAZI UNIVERSITY JOURNAL OF SCIENCE, cilt.32, ss.823-831, 2019 (ESCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 32 Konu: 3
  • Basım Tarihi: 2019
  • Doi Numarası: 10.35378/gujs.471859
  • Dergi Adı: GAZI UNIVERSITY JOURNAL OF SCIENCE
  • Sayfa Sayıları: ss.823-831

Özet

Today, the amount of biological data types obtained are increasing every day. Among these data types are micro arrays that play an important role in cancer diagnosis. The data analysis that are carried out through traditional approaches have proven unsuccessful in delivering efficient results on data types where data complexity is high and where sampling is low. For this reason, using a hybrid algorithm by merging the effective features of two distinct algorithms will yield effective results. In this study, a classification process was performed firstly by dimension reduction on micro array data that were obtained from the tissues from patients with a tumor in their central nervous system and then by using an artificial neural network algorithm that was optimized through Fire Fly Algorithm (FF), a hybrid approach. The data obtained were compared to K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) classification algorithms, which are frequently used in the literature. Also, the results were compared to the fmdings that were obtained from artificial neural networks, which are reinforced by Genetic Algorithm (GA), another hybrid approach. Then the results were shared. The performance results obtained show that hybrid approaches present a highly precise and more efficient classification process but they show a slower performance than basic classification algorithms.