Hopfield neural network and fuzzy Hopfield neural network for diagnosis of liver disorders

Nowadays, artificial intelligence has a wide usage especially for designing intelligent systems in medicine. Diagnosing and determining different kinds of diseases are a part of this system's duties. In this research tried to diagnose liver disorders more accurate by using Hopfield neural network and fuzzy Hopfield beside fuzzy C-Means. Requiring data including 345 records and 6 fields is chosen from valid data bank (UCI) there are 6 inputs and the rate of network liver disorders risk is the output. In comparison with traditional diagnoses this system is faster, more economical, more reliable and more accurate. In the best state of training, Hopfield neural network and fuzzy Hopfield neural network diagnose liver disorders with the accuracy of 88.2% and 92% respectively. These results have been examined and proved experimentally under observation of specialists. Regarding diverse neural networks which been applied in diagnosing liver disorders, results have been an agreeable improvement. Refereed/Peer-reviewed