Fuzzy logic alternative for analysis in the biomedical sciences
Computers and Biomedical Research
Artificial Intelligence in Medicine
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
Computers in Biology and Medicine
Classification of transcranial Doppler signals using their chaotic invariant measures
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
A new approach to intelligent fault diagnosis of rotating machinery
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Applied Soft Computing
An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines
Expert Systems with Applications: An International Journal
Fuzzy logic based gait classification for hemiplegic patients
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
A survey on use of soft computing methods in medicine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients
Expert Systems with Applications: An International Journal
A decision support system for EEG signals based on adaptive fuzzy inference neural networks
Journal of Computational Methods in Sciences and Engineering
Computers in Biology and Medicine
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In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of internal carotid artery stenosis and occlusion. The internal carotid arterial Doppler signals were recorded from 130 subjects that 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects. The three ANFIS classifiers were used to detect internal carotid artery conditions (normal, stenosis and occlusion) when two features, resistivity and pulsatility indices, defining changes of internal carotid arterial Doppler waveforms were used as inputs. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of internal carotid artery stenosis and occlusion were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of classification accuracies and the results confirmed that the proposed ANFIS classifiers have some potential in detecting the internal carotid artery stenosis and occlusion. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.