Hierarchical mixtures of experts and the EM algorithm
Neural Computation
A connectionist method for pattern classification with diverse features
Pattern Recognition Letters
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
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
Wavelet/mixture of experts network structure for EEG signals classification
Expert Systems with Applications: An International Journal
Adaptive mixtures of local experts
Neural Computation
A sequential neural network model for diabetes prediction
Artificial Intelligence in Medicine
Journal of Medical Systems
Detection of Resistivity for Antibiotics by Probabilistic Neural Networks
Journal of Medical Systems
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Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of diabetics and subjects having risk factors of diabetes. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and their performances in detection of diabetics were compared. The performance of the classification algorithms was illustrated on the Pima Indians diabetes data set. The present research demonstrated that the modified mixture of experts (MME) achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems.