Modified bio-inspired algorithms for diagnosis of breast cancer using aggregation. (5th March 2021)

• Record Type: Journal Article
• Authors: Sharma, Moolchand; Gupta, Shubbham; Deswal, Suman
• Is Part Of: International journal of innovative computing and applications. Volume 12:Number 1(2021)
• Page Start: 37
• Page End: 47
• Publication Date: 2021-03-05
• Subjects: breast cancer -- bio-inspired algorithms -- aggregation -- particle swarm optimisation -- PSO -- firefly algorithm -- feature selection -- linear support vector machine -- SVM -- decision tree -- K-nearest neighbour -- KNN -- random forest classifiers -- RFCs; 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

The most widely detectable of all cancers found in women is breast cancer. The mortality rate is also the second-highest among women with a 12% growth rate. It is very pertinent to diagnose breast cancer in the nascent stages so that the survival of the patient is ensured with the help of proper medication. Several algorithms have been proposed in this regard. However, they have failed to achieve the desired level of accuracy. An improved version of the particle swarm optimisation and firefly algorithm is presented in this paper to overcome the drawbacks of the existing algorithm. The two algorithms are further aggregated to improve the accuracy of the results. The aggregated algorithm is used on the Breast Cancer Wisconsin (Diagnostic) Data Set (real-valued dataset), and results are calculated for different classifiers. An accuracy of 92%-96% is shown by improved particle swarm optimisation and 1%-2% overall hike in the accuracy by improved firefly algorithm, respectively. Finally, the aggregated algorithm shows an accuracy of 93%-97%. Further, random forest classifier has displayed the best accuracy of 97%.