Application of a new logic domain method for the diagnosis of hepatocellular carcinoma.

In this paper we describe the application of a new learning tool for the diagnosis of hepatocellular carcinoma. The method adopted operates in the logic domain and presents several interesting features for the development of medical diagnostic systems. We consider a database of 128 patients, 64 of which affected by hepatocellular carcinoma, while the others affected by cirrhosis but not from hepatocellular carcinoma. Each patient is described by a number of attributes measured in non-invasive way. The system, after the training, is able to correctly separate the 64 patients affected by cirrhosis from the others 64 affected by hepatocellular carcinoma and is now ready to produce automatic diagnosis for new patients. The hepatocellular carcinoma is one of the most widely spread malignant tumors in the world. The ability to detect the tumor in its early stages in a minimally invasive way is crucial to the treatment of patients with this disease.