Bio-inspired algorithms for diagnosis of breast cancer. (30th October 2019)

• Record Type: Journal Article
• Authors: Sharma, Moolchand; Gupta, Shubbham; Sharma, Prerna; Gupta, Deepak
• Is Part Of: International journal of innovative computing and applications. Volume 10:Number 3/4(2019)
• Page Start: 164
• Page End: 174
• Publication Date: 2019-10-30
• Subjects: breast cancer -- bio-inspired algorithms -- artificial bee colony optimisation -- particle swarm optimisation -- ant colony optimisation -- firefly algorithm -- feature selection -- decision tree -- linear support vector machines -- K-nearest neighbour -- random forest classifiers; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Most commonly found cancer among women is breast cancer. Roughly 12% of women grow breast cancer during their lifetime. It is the second prominent fatal cancer among women. Breast cancer diagnosis is necessary during its initial phase for the proper treatment of the patients to lead constructive lives for an extensive period. Many different algorithms are introduced to improve the diagnosis of breast cancer, but many have less efficiency. In this work, we have compared different bio-inspired algorithms including artificial bee colony optimisation, particle swarm optimisation, ant colony optimisation and firefly algorithm. The performances on these algorithms have been measured for UCI Dataset of Wisconsin Diagnostic Breast Cancer, and the results have been calculated using different classifiers on the selected features. After the experiment, it is seen that BPSO has shown maximum accuracy of 96.45% and BFA has shown considerable results of 95.81% with six features which is minimum of all algorithms.