Comparison of NEAT and Backpropagation Neural Network on Breast Cancer Diagnosis

Hamza Turabieh
2016 International Journal of Computer Applications  
In this paper we present a comparison between NeuroEvolution of Augmenting Typologies (NEAT) algorithm with Backpropagation Neural Network for the prediction of breast cancer. Machine learning algorithms could be used to enhance the performance of medical practitioners in the diagnosis of breast cancer. NEAT is a promising machine learning algorithm, which combines genetic algorithms and neural network. We compare the performance of these two algorithms on a standard benchmark dataset. Our
more » ... ts demonstrate that NEAT outperforms Backpropagation Neural Network, and we show that experimentally that NEAT has better generalization and much lower computational cost. NEAT, Backpropagation, Breast Cancer. Jong et al. [13] applied hybrid Bayesian network and standard ANN to predict breast cancer. The accuracy of the proposed hybrid is 87.2%, while the accuracy of the standard ANN is 88.8%. There are several research papers that Keywords
doi:10.5120/ijca2016909245 fatcat:pufhpgvbgndw3m5ujdtiinjdli