Structural Redesign of Artificial Neural Network for Predicting Breast Cancer with the Aid of Artificial Bee Colony

Abstract

Objectives: In apparent, the core intention is to predict breast cancer stage such as benignant or malignant with different techniques from Breast Cancer Wisconsin (original) benchmark dataset. Methods/Statistical Analysis: When compared through every other tumor, breast cancer is solitary of the actual causes for death in women. To forecast the result of several diseases or find genetic activities of tumors, the breast cancer data could be valuable from the classification. In this work, the proposed method is Artificial Neural Network (ANN) classification with Artificial Bee Colony Optimization (ABC) technique. Findings: Artificial Neural Network (ANN) structure is worked and in this structure training algorithms is utilized and the proposed is Levenberg-Marquardt technique. Artificial Bee Colony Optimization (ABC) technique is used to optimize the hidden layer and neuron of ANN. In the outcome, best validation performance is predicted and the different execution assessment measurements for two optimization algorithms are investigated. Application/Improvements: The comparison performance graph for Accuracy, Sensitivity and Specificity are foreseeing for the most part the precision worth is 95.9% in favor of Artificial Bee Colony Optimization technique. Keywords: ANN and ABC, Levenberg-Marquardt