Abstract
The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks like diagnosis and treatment planning. These kinds of estimations are currently performed by clinicians using non-numerical techniques. Artificial neural networks are shown to be a powerful tool for analyse data sets where there are complicated non-linear interactions between the input data and the information to be predicted, and fuzzy logic appears as an useful tool to perform decision making in real life problems. In this paper, we present an hybrid neuro-fuzzy prognosis system for the prediction of patients relapse probability using clinical-pathological data (tumor size, patient age, estrogens receptors, etc.) from the Medical Oncology Service of the Hospital Clinical University of Malaga. Results show the classification accuracy improvement obtained by the proposed model in comparison with an approach based on exclusively on artificial neural networks proposed in our previous work.
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Jerez Aragonés, J.M., Peláez Sánchez, J.I., Doña, J.M., Alba, E. (2004). A Neuro-fuzzy Decision Model for Prognosis of Breast Cancer Relapse. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, JL. (eds) Current Topics in Artificial Intelligence. TTIA 2003. Lecture Notes in Computer Science(), vol 3040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25945-9_63
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DOI: https://doi.org/10.1007/978-3-540-25945-9_63
Publisher Name: Springer, Berlin, Heidelberg
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