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Cluster Computing

, Volume 22, Supplement 6, pp 14035–14047 | Cite as

Enhanced firefly algorithm (EFA) based gene selection and adaptive neuro neutrosophic inference system (ANNIS) prediction model for detection of circulating tumor cells (CTCs) in breast cancer analysis

  • T. S. UmamaheswariEmail author
  • P. Sumathi
Article

Abstract

In the course of recent decades, substantial progress has been made in the analysis and treatment of breast cancer (BC). Early recognizable proof of relapsed and metastatic disease has been an essential concentration of continuous research. Circulating tumor cells (CTCs) are embroiled as harbingers of metastases. With propels in location advances, CTCs offer the choice for real-time fluid biopsies. Techniques to distinguish CTCs in the bloodstream system by physical or biochemical properties, although feasible, still expect upgrades to advance broad, reproducible utilize. In this examination, address the issue of simultaneous gene selection and robust classification of BC tests by exhibiting two hybrid algorithms, in particular enhanced firefly algorithm (EFA) and adaptive neuro neutrosophic inference system (ANNIS) with chose qualities for CTC identification. It comprises of three noteworthy stages: In the primary stage intends to remove quality marks related with pairwise separation between cell composes. In the second stage, a novel meta-heuristic algorithm in light of EFA is proposed to distinguish prescient qualities for BC prediction. In EFA algorithm, FAs has been altered by utilizing the discrete space as the continuous space. The EFA algorithm adaptively balances the investigation and misuse to rapidly locate the optimal solution. EFA is another developmental computation method, motivated by the blaze lighting procedure of fireflies. The EFA can rapidly look the gene space optimal or near-optimal gene subset amplifying a given fitness work. The proposed fitness work utilized joins both classification precision and feature reduction measure. At long last, ANNIS is produced to precisely order the examples into the essential and auxiliary illness. The results demonstrate that proposed EFA-ANNIS performs better when contrasted with Adaptive Neuro Fuzzy Inference System, Fuzzy Neural Network, Bayesian Least Absolute Shrinkage and Selection Operator classifier and Support Vector Machine can help in removing more informative genes supporting to building elite forecast models. The results of the classifiers are measured in terms of the false positive rate, false negative rate, precision, recall, F-measure, accuracy and error.

Keywords

Breast cancer (BC) Circulating tumor cells (CTCs) Gene selection Classification Enhanced firefly algorithm (EFA) and adaptive neuro neutrosophic inference system (ANNIS) Breast cancer analysis Peripheral blood (PB) 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.R&D Centre, Bharathiar UniversityCoimbatoreIndia
  2. 2.PG & Research Department of Computer ScienceGovernment Arts CollegeCoimbatoreIndia

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