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 online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy-neural system modeling
IEEE Transactions on Fuzzy Systems
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This paper presents an assessment of liver function using novel neuro-fuzzy model of blood albumin level BA. The developed model that is used to predict the BA consists of four inputs: Asparate Aminotransferace AST, Alkaline Phosphate ALP, Total Bilirubin T. Bil. and Total Protein T. Prot., which are measured in any routine liver function test. The proposed BA model was trained using 211 measured data and a root-mean square error RMSE of 0.29 for 100 epochs was achieved. The performance of the developed BA model was validated using 57 testing data sets and RMSE of 0.34 for 100 epochs was achieved. The correlation coefficient CC between the predicted and measured values of blood albumin is statistically significant CC=0.83, which ensures the efficiency and accuracy of developed fuzzy model for predicting BA. The main clinical benefit of this model is that it improves the assessment capabilities of liver diseases and can be used as an integral part of any medical expert system denoted for assessment and diagnosis of liver disorders.