Disease Diagnosis Using Query-Based Neural Networks
Resource
Lecture Notes in Computer Science, 3498, 767-773
Journal
Advances in Neural Networks
Pages
767-773
Date Issued
2005
Date
2005
Author(s)
Hutchison, David
Kanade, Takeo
Kittler, Josef
Kleinberg, Jon M.
Mattern, Friedemann
Mitchell, John C.
Naor, Moni
Nierstrasz, Oscar
Rangan, C. Pandu
Steffen, Bernhard
Sudan, Madhu
Terzopoulos, Demetri
Tygar, Dough
Vardi, Moshe Y.
Weikum, Gerhard
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. © Springer-Verlag Berlin Heidelberg 2005.
Type
journal article
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