Abstract
In this paper, a new approach using ANFIS as a diagnosis system on WBCD problem is proposed. The automatic diagnosis of breast cancer is an important, real-world medical problem. It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the relationships between the large measured factors. It is possibly resolved with using AI algorithms. ANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks. Therefore, it can deal with ambiguous data and learn from the past data. Applying ANFIS as a diagnostic system was considered in our experiment. In addition, the computational performance of diagnosis system is an important issue as well as the output correctness of the inference system. Methods of using recommended inputs generated by the Genetic-Algorithm, Decision-Tree and Correlation-Coefficient computation with ANFIS was proposed to reduce the computational overhead.
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© 2005 Springer-Verlag Berlin Heidelberg
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Song, H., Lee, S., Kim, D., Park, G. (2005). New Methodology of Computer Aided Diagnostic System on Breast Cancer. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_124
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DOI: https://doi.org/10.1007/11427469_124
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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