Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Neural computing: an introduction
Neural computing: an introduction
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Determination of Coronary Failure with the Application of FFT and AR Methods
Journal of Medical Systems
Classification of Transcranial Doppler Signals Using Artificial Neural Network
Journal of Medical Systems
IEEE Transactions on Neural Networks
Journal of Medical Systems
A Radial Basis Function Neural Network Model for Classification of Epilepsy Using EEG Signals
Journal of Medical Systems
Diagnosing diabetes using neural networks on small mobile devices
Expert Systems with Applications: An International Journal
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For the classification of Middle Cerebral Artery (MCA) stenosis, Doppler signals have been received from the diabetes and control group by using 2 MHz Transcranial Doppler. After the Fast Fourier Transform (FFT) analyses of the Doppler signals, Power Spectrum Density (PSD) estimations have been made and Multilayer Perceptron (MLP) and Radial Basis Function (RBF) have been dealt to apply to the neural networks. PSD estimations of Doppler signals received from MCA of 104 subjects have been successfully classified by MLP (correct classification = 94.2%) and RBF (correct classification = 88.4%) neural network. As we have seen in the area under ROC curve (AUC), MLP neural network (AUC = 0.934) has classified more successfully when compared with RBF neural network (AUC = 0.873).