Medical databases hold diagnostic information based on patient medical records. However, these medical records may not always hold enough information for standard learning algorithms. Sometimes contradictory cases may occur, in which the pattern of input signals is the same, but the pattern of output signals is starkly different. In this paper, we apply a learning method of the immune multi agent neural networks (IMANNs) to the medical disease databases. IMANNs have agents of the macrophages, B-cells, and T-cells. Each agent employs a different type of neural networks. Because their agents work cooperatively and competitively, IMANNs can classify training dataset into some subsets automatically, and successively each B-cell agent trains specially for the divided training subset. In order to verify the effectiveness of our proposed method, we tested the coronary heart disease database as medical databases.
['Shinichi Oeda', 'Takumi Ichimura', 'Katsumi Yoshida']
Immune Multi Agent Neural Network and Its Application to the Coronary Heart Disease Database