Neural Networks
Advanced algorithms for neural networks: a C++ sourcebook
Advanced algorithms for neural networks: a C++ sourcebook
Probabilistic neural networks and general regression neural networks
Fuzzy logic and neural network handbook
An application of heuristic search methods to edge and contour detection
Communications of the ACM
A Neural Network Model for Prognostic Prediction
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Image processing in case-based reasoning
The Knowledge Engineering Review
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Evaluation of shape similarity measurement methods for spine X-ray images
Journal of Visual Communication and Image Representation
Registration and retrieval of highly elastic bodies using contextual information
Pattern Recognition Letters
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
IEEE Transactions on Information Technology in Biomedicine
Entropy maximization networks: an application to breast cancer prognosis
IEEE Transactions on Neural Networks
Heuristic pattern correction scheme using adaptively trained generalized regression neural networks
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Building a qualitative recruitment system via SVM with MCDM approach
Applied Intelligence
Learning SVM with weighted maximum margin criterion for classification of imbalanced data
Mathematical and Computer Modelling: An International Journal
Optimizing the modified fuzzy ant-miner for efficient medical diagnosis
Applied Intelligence
Transductive cost-sensitive lung cancer image classification
Applied Intelligence
Extended fuzzy c-means: an analyzing data clustering problems
Cluster Computing
A knowledge-based architecture for the management of patient-focused care pathways
Applied Intelligence
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In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning techniques. In this paper we propose a Support Vector Machines (SVMs) based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease. The paper provides the implementation details along with the corresponding results for all the assessed classifiers. Several comparative studies have been carried out concerning both the prognosis and diagnosis problem demonstrating the superiority of the proposed SVM algorithm in terms of sensitivity, specificity and accuracy.