Computer
Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
An Introduction to Neural Networks
An Introduction to Neural Networks
Machine Learning
Artificial Neural Networks in Medicine and Biology: Proceedings of the Annimab-1 Conference, Goteborg, Sweden, 13-16 May 2000
A novel approach for ANFIS modelling based on full factorial design
Applied Soft Computing
Using neural networks to monitor supply chain behaviour
International Journal of Computer Applications in Technology
Adaptive neuro-fuzzy system as a novel approach for predicting post-dialysis urea rebound
International Journal of Intelligent Systems Technologies and Applications
New improved FLANN approach for dynamic modelling of sensors
International Journal of Computer Applications in Technology
A robust self-tuning fuzzy PI scheme for DTC induction motor drive
International Journal of Computer Applications in Technology
Application of interval type-2 fuzzy neural networks to predict short-term traffic flow
International Journal of Computer Applications in Technology
Neural computation in medicine
Artificial Intelligence in Medicine
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
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The blood urea concentration has been used as a surrogate marker for toxin elimination in hemodialysed patients, and several indices based on it have been proposed in recent years for monitoring treatment adequacy. Measuring urea nitrogen concentrations at the inlet and outlet of dialyser is crucial to evaluate the in-vivo blood side dialyser urea clearance during hemodialysis. Although frequent measurement is needed to avoid inadequate dialysis efficiency, artificial intelligence can repeatedly perform the forecasting tasks and may be a satisfactory substitute for laboratory tests. Neuro-fuzzy technology represents a promising forecasting application in clinical medicine. In this study, two fuzzy models have been proposed to predict dialyser inlet and outlet urea concentrations in order to estimate dialyser clearance without blood sampling. The model is of multi-input single-output MISO type. Multi-adaptive neuro-fuzzy inference system MANFIS technique of fuzzy-based systems has been employed. The performance of the model is authenticated by evaluating the predicted results with the practical results obtained by conducting the confirmation experiments. The results suggest that the neuro-fuzzy technology, based on limited clinical parameters, is an excellent alternative method for accurately predicting arterial and venous urea concentrations in hemodialysis patients. The proposed model can be used for intelligent online adaptive system.