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Comparison of NEAT and Backpropagation Neural Network on Breast Cancer Diagnosis
| Content Provider | Semantic Scholar |
|---|---|
| Author | Turabieh, Hamza |
| Copyright Year | 2016 |
| Abstract | 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 results demonstrate that NEAT outperforms Backpropagation Neural Network, and we show that experimentally that NEAT has better generalization and much lower computational cost. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijcaonline.org/archives/volume139/number8/24514-24514-2016909245?format=pdf |
| Alternate Webpage(s) | https://www.ijcaonline.org/archives/volume139/number8/24514-24514-2016909245?format=pdf |
| Alternate Webpage(s) | http://www.ijcaonline.org/research/volume139/number8/turabieh-2016-ijca-909245.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithmic efficiency Artificial neural network Backpropagation Benchmark (computing) Breast Carcinoma Computation Experiment Generalization (Psychology) Machine learning Malignant neoplasm of breast Mammary Neoplasms Neural Network Simulation Neuroevolution of augmenting topologies Silo (dataset) |
| Content Type | Text |
| Resource Type | Article |
