Ovarian cancer diagnosis using complementary learning fuzzy neural network

DNA microarray is an emerging technique in ovarian cancer diagnosis. However, very often, microarray data is ultra-huge and difficult to analyze. Thus, it is desirable to utilize fuzzy neural network (FNN) approach for assisting the diagnosis and analysis process. Amongst FNN, complementary learning FNN is able to rapidly derive fuzzy sets and formulate fuzzy rules. Complementary learning FNN uses positive and negative learning, and hence it subsides the effect of curse of dimension and is capable of modeling the dynamics of problem space with relative good classification performance. Furthermore, FALCON-AART has human-like reasoning that allows physician to examine its computation in a familiar way. FALCON-AART can generate intuitive fuzzy rule to justify its reasoning, which is important to generate trust among the users of the system. Hence, FALCON-AART is applied in ovarian cancer diagnosis as a clinical decision support system in this work. Its experimental results are encouraging.