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2015 | OriginalPaper | Chapter

A Comparison of Hybrid Neural Network Based Breast Cancer Diagnosis Systems

Authors : Hsine-Jen Tsai, Hao-Chun Lu, Tung-Huan Wu, Chiang-Sheng Lee

Published in: HCI in Business

Publisher: Springer International Publishing

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Abstract

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.

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Metadata
Title
A Comparison of Hybrid Neural Network Based Breast Cancer Diagnosis Systems
Authors
Hsine-Jen Tsai
Hao-Chun Lu
Tung-Huan Wu
Chiang-Sheng Lee
Copyright Year
2015
Book
HCI in Business

Print ISBN: 978-3-319-20894-7

Electronic ISBN: 978-3-319-20895-4

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-20895-4_59

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