Machine learning is the one of the famous
Artificial Intelligence (AI) technique. Data Mining or Machine
Learning techniques are most popular in medical diagnosis,
classification, forecasting etc. K-Nearest Neighbor, SVM
(Support Vector Machine), DT (Decision Tree),RF (Random
Forest),NN (Neural Network) are famous classification
algorithms. Neural Network is one of the popular techniques,
which is used to refine the verdict of breast cancer. A neural
network is otherwise known as Artificial Neural Network(ANN),
which is mimicking of biological neurons of human brain.
Genetic Algorithm (GA) is emerged bio inspired technique.
Selection, Crossover, and Mutation are three operations in
Genetic Algorithm. The performance of a genetic algorithm
depends on the genetic operators, particularly crossover operator.
Grey Wolfoptimization algorithm is inspired from hunting of wolf
strategy. Alphas, Beta, Gamma are the three levels ofprocesses.
In this paper, a novel hybrid Genetic Grey Wolf based Neural
Network is introduced and we named it as G2NN. In the field of
medical, we need more accuracy when compared to other field,
because it relates to human life. Many researchers found new
novel ideas for breast cancer data classification using neural
network model. Among many diseases,Breast Cancer is one of
the unsafe diseases among women in Indiaand in addition to the
whole world. The early detection of cancer helps in curing the
disease completely. In many research areas Genetic Algorithm
and Grey wolf algorithm are used to train neurons in order to
yield good accuracy. In this manuscript, a new GeneticGrey Wolf
optimizer based Neural Network is introduced and we compare
the proposed work with other techniques like SVM(Support
Vector Machine),NN (Neural Network), Genetic based Neural
Network, Grey wolf based Neural Network and the experimental
results of proposed work produced better result. The proposed
algorithm produces 98.9 % of accuracy on UCI Wisconsin breast
cancer dataset.