Neural Networks
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
A novel large-memory neural network as an aid in medical diagnosis applications
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Integrated feature architecture selection
IEEE Transactions on Neural Networks
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Learning vector quantization for the probabilistic neural network
IEEE Transactions on Neural Networks
AR Spectral Analysis Technique for Human PPG, ECG and EEG Signals
Journal of Medical Systems
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Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. The noise in a classification model can be reduced by identifying a set of salient features and then more accurate classification can be obtained. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of probabilistic neural networks (PNNs) used in classification of internal carotid arterial Doppler signals (ICADS). In order to extract features representing the ICADS, model-based methods were used. The PNNs used in the ICADS classification were trained for the SNR screening method. The application results of the SNR screening method to the ICADS demonstrated that classification accuracies of the PNNs with salient input features are higher than that of the PNNs with salient and nonsalient input features.