Disease diagnosis using query-based neural networks

Authors:
Ray-I Chang
Affiliations:
Department of Engineering Science & Oce. Engineering, National Taiwan University, Taipei, Taiwan, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

The ability of high tolerance for learning-by-example makes neural networks flexible and powerful in resolving various application problems. However, while being applied in real world, the time required to induce models from large data sets should be considered. In this paper, we apply QSS (Query-based learning with Selective-attention and Self-regulation) to back-propagation neural networks for resolving the data classification problem in biomedical applications. Results show that the proposed method can significantly reduce the training set cardinality. Additionally, the quality of training results can be ensured. It provides a powerful tool to help physicians analyze, model and make sense of complex clinical data for disease diagnosis.