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

Year 2019, , 823 - 831, 01.09.2019

Mehmet Bılen , Ali Hakan Isık , Tuncay Yıgıt

https://doi.org/10.35378/gujs.471859

Cited By: 1

Abstract

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 findings 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.

Keywords

Cancer diagnosis, firefly algorithm, artificial neural networks, hybrid algorithms, optimization

References

• Petalidis, L. P., Oulas, A., Backlund, M., Wayland, M. T., Liu, L., Plant, K., et al. Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data. Mol Cancer Ther, 7(5), 1013-1024,(2008).
• Ellis, M., Davis, N., Coop, A., Liu, M., Schumaker, L., Lee, R. Y., et al. Development and Validation of a Method for Using Breast Core Needle Biopsies for Gene Expression Microarray Analyses. Clinical Cancer Research, 8, 1155-1166,(2002).
• Peterson, L. E., & Coleman, M. A. Machine learning-based receiver operating characteristic (ROC) curves for crisp and fuzzy classification of DNA microarrays in cancer research. International Journal of Approximate Reasoning, 47(1), 17-36,(2008).
• Bicciato, S., Pandin, M., Didone, G., & Bello, C. D. Pattern identification and classification in gene expression data using an autoassociative neural network model. Biotechology and Bioengineering, 81, 594-606,(2002).
• Peng, S., Xu, Q., Ling, X. B., Peng, X., Du, W., & Chen, L. Molecular classification of cancer types from microarray data using combination of genetic algorithms and support vector machines. FEBS Letters, 555 (2) 358-362,(2003).
• Li, L., Jiang, W., Li, X., Moser, K. L., Guo, Z., Du, L., et al. A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. Genomics, 85(1) 16-23,(2005).
• Rao, A. C., Somayajulu, D., Banka, H., & Chaturverdi, R. Outliner Detection in Micoarray Data Using Hybrid Evolutionary Algorithm. Proceida Technology, 6 291-298,(2012).
• Kumar, P. G., Victoire, T. A., Renukadevi, P., & Devaraj, D. Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm. Expert Systems with Applications, 39 (2) 1811-1821,(2012).
• Bilen, M., Işık, A. H., & Yiğit, T. Mikro Dizi verilerinin Sınıflandırılması için Melez bir Yapay Sinir Ağı- Genetik Algoritma Yaklaşımı. SIU-2015:Sinyal İşleme ve İletişim Uygulamaları Kurultayı, (s. 243-246). Malatya,(2015).
• Blanton, H. An introduction to neural networks for technicians, engineers and other non PhDs. Proceedings of the 1997 Artificial Neural Networks in Engineering Conference. St. Louis: ANNIE'97,(1997).
• Yang, X. S. Naute-Inspired Metaheuristic Algorithms. UK: Luniver Press,(2008).
• Pomeroy, S. L., Tamayo, P., Gaasenbeek, M., Sturla, L. M., & Angelo, M. Prediction of central nervous system embryonal tumor outcome based on gene expression. Letter to Nature, 436-442,(2002).