Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Machine Learning
Using neural networks to aid the diagnosis of breast implant rupture
Computers and Operations Research
Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Adaptive mixtures of local experts
Neural Computation
A general regression neural network
IEEE Transactions on Neural Networks
Model selection for medical diagnosis decision support systems
Decision Support Systems
A Mixture of Experts Network Structure for Breast Cancer Diagnosis
Journal of Medical Systems
Combining Neural Network Models for Automated Diagnostic Systems
Journal of Medical Systems
Extracting drug utilization knowledge using self-organizing map and rough set theory
Expert Systems with Applications: An International Journal
Implementing automated diagnostic systems for breast cancer detection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Combined neural network model employing wavelet coefficients for EEG signals classification
Digital Signal Processing
Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer
Journal of Medical Systems
Artificial Intelligence in Medicine
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
Engineering Applications of Artificial Intelligence
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Disease modeling using evolved discriminate function
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Artificial Intelligence in Medicine
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Hybrid ensemble approach for classification
Applied Intelligence
An intelligent model for the classification of children's occupational therapy problems
Expert Systems with Applications: An International Journal
Self-organizing map for cluster analysis of a breast cancer database
Artificial Intelligence in Medicine
Artificial neural networks applied to cancer detection in a breast screening programme
Mathematical and Computer Modelling: An International Journal
Breast cancer detection using cartesian genetic programming evolved artificial neural networks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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There are a number of different quantitative models that can be used in a medical diagnostic decision support system (MDSS) including parametric methods (linear discriminant analysis or logistic regression), non-parametric models (K nearest neighbor, or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. Practitioners are left to either choose a favorite model or to test a small subset using cross validation methods. This paper illustrates the use of a self-organizing map (SOM) to guide model selection for a breast cancer MDSS. The topological ordering properties of the SOM are used to define targets for an ideal accuracy level similar to a Bayes optimal level. These targets can then be used in model selection, variable reduction, parameter determination, and to assess the adequacy of the clinical measurement system. These ideas are applied to a successful model selection for a real-world breast cancer database. Diagnostic accuracy results are reported for individual models, for ensembles of neural networks, and for stacked predictors.