A Comparison of Hybrid Neural Network Based Breast Cancer Diagnosis Systems
| Autor: | Hao-Chun Lu, Chiang-Sheng Lee, Hsine-Jen Tsai, Tung-Huan Wu |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Artificial neural network
Association rule learning Computer science business.industry Dimensionality reduction medicine.disease Machine learning computer.software_genre Hybrid neural network Correlation Breast cancer Hybrid system Genetic algorithm medicine Artificial intelligence business computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319208947 HCI (23) |
| Popis: | Breast cancer is the second leading cause of death among the women aged between 40 and 59 in the world. The diagnosis of such disease has been a challenging research problem. With the advancement of artificial intelligence in medical science, numerous AI based breast cancer diagnosis system have been proposed. Many researches combine different algorithms to develop hybrid systems to improve the diagnosis accuracy. In this study, we propose three artificial neural network based hybrid diagnosis systems respectively combining association rule, correlation and genetic algorithm. The effectiveness of these systems is examined on Wisconsin Breast Cancer Dataset. We then compare the accuracy of these three hybrid diagnosis systems. The results indicated that the neural network combining with association rule not only has excellent dimensionality reduction ability but also has the similar accurate prediction with correlation based neural network which has best accurate prediction rate among all three systems compared. |
| Databáze: | OpenAIRE |
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