Creating a model based on artificial neural network for liver cirrhosis diagnose

Liver cirrhosis has acquired a great importance on both national and global levels due to the growing number of ill persons and nevertheless to serious complication associated to it. Worldwide liver cirrhosis (liver cirrhosis) represents the tenth leading cause of death according to recent statistical data reported by the World Health Organization: The prior concern of medical science for is to establish an effective diagnostic algorithm for liver cirrhosis and to implement therapeutic protocols in order to achieve an adequate management of complications. Time based the correct diagnose of liver cirrhosis can be essential in order to prevent further liver damage. That is translated in according the ill patient a real chance for transplantation and preventing decompensation risk factors for this condition. The main goal of this paper is to design a noninvasive method based on an artificial neural network model that will serve to diagnose liver cirrhosis patients by using only laboratory data. The prospective study included patients with various etiologies liver cirrhosis hospitalized or treated in the Gastroenterology Clinic of the Emergency Hospital “St. Andrew” from Galati which have been monitored every 3 months for one year.