Data differentiation and parameter analysis of a chronic hepatitis B database with an artificial neuromolecular system

This paper describes the application of a biologically motivated system to the diagnosis of chronic hepatitis B. The system integrates intra- and inter-neuronal information processing so as to capture the biology-like gradual transformability of structure/function relationships. The system was applied to a clinical hepatitis B database, divided into two sets. The first set comprised 676 records, of which one half were chronic hepatitis B patients, and the other half healthy individuals. The second set included 375 records, of which one third were chronic hepatitis B patients; another third were hepatitis B carriers, and the remaining third healthy non-carriers. Each record consisted of ten examination items. Experimental results showed that the system was able to correctly differentiate 99.3 and 91.2% of the records in the first and the second sets, respectively. Differentiation means making a distinction between different categories of data in each set. After substantial learning with the first set, the system was then tested with the second set, and it was able to correctly differentiate 95.7% of the records, suggesting a high differentiating capability in this system. This system demonstrated an effective self-organizing capability in determining significant and insignificant examination items from patterns of the clinical data. It also showed that some combinations of these items were more effective for determining whether one is infected with chronic hepatitis B than others.