Hierarchical mixtures of experts and the EM algorithm
Neural Computation
An improved neural classification network for the two-group problem
Computers and Operations Research
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A combined neural network and decision trees model for prognosis of breast cancer relapse
Artificial Intelligence in Medicine
Model selection for a medical diagnostic decision support system: a breast cancer detection case
Artificial Intelligence in Medicine
Implementing automated diagnostic systems for breast cancer detection
Expert Systems with Applications: An International Journal
Modified Mixture of Experts for Diabetes Diagnosis
Journal of Medical Systems
Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer
Journal of Medical Systems
Journal of Medical Systems
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Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide diagnosing of breast cancer. Expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. Specifically, diagnosis is an attempt to accurately forecast the outcome of a specific situation, using as input information obtained from a concrete set of variables that potentially describe the situation. The ME network structure was implemented for breast cancer diagnosis using the attributes of each record in the Wisconsin breast cancer database. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. For the Wisconsin breast cancer diagnosis problem, the obtained total classification accuracy by the ME network structure was 98.85%. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.