A Neural Network Based Model for Prognosis of Early Breast Cancer

The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Survival estimations are currently performed by clinicians using non-numerical techniques. Artificial neural networks are shown to be a powerf...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2004-05, Vol.20 (3), p.231-238
Main Authors: Gómez-Ruiz, J.A., Jerez-Aragonés, J.M., Muñoz-Pérez, J., Alba-Conejo, E.
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Breast cancer
Cancer surgery
Medical prognosis
Medicine
Network topologies
Neural networks
Prognosis
Studies
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Summary:The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Survival estimations are currently performed by clinicians using non-numerical techniques. Artificial neural networks are shown to be a powerful tool for analyzing data sets where there are complicated nonlinear interactions between the input data and the information to be predicted. In this paper, a new estimation to set the maximum bound on prediction accuracy is presented, based on the approximation of the a posteriori probability of Bayes by feed-forward three-layer neural networks. This result is applied to different patients' follow-up time intervals, in order to obtain the best prediction accuracy for the correct classification probability of patient relapse after breast cancer surgery using clinical-pathological data (tumor size, patient age, menarchy age, etc.), which were obtained from the Medical Oncology Service of the Hospital Clinico Universitario of Malaga, Spain. Different network topologies and learning parameters are investigated to obtain the best prediction accuracy. The actual results show as, after training process, the final model is appropriate to make predictions about the relapse probability at different times of follow-up.[PUBLICATION ABSTRACT]
ISSN:0924-669X
1573-7497
DOI:10.1023/B:APIN.0000021415.88365.c4