THYROID DISEASE DETECTION USING MODIFIED FUZZY HYPERLINE SEGMENT CLUSTERING NEURAL NETWORK

Two common diseases of the thyroid gland, which releases thyroid hormones for regulating the rate of body’s metabolism, are hyperthyroidism and hypothyroidism. The classification of these thyroid diseases is a one of the considerable tasks.? In this work we propose modified fuzzy hyperline segment clustering neural network (MFHLSCNN) for classification of thyroid disease diagnosis. The MFHLSCNN algorithm is suitable for clustering and classification. This algorithm can learn ill-defined nonlinear cluster boundaries in a few passes and is suitable for on-line adaptation. The work is extension of fuzzy hyperline segment clustering neural network (FHLSCNN) proposed by Kulkarni U. V. and Sontakke T. R. Both the algorithms utilize fuzzy sets as pattern clusters in which each fuzzy set is an union of fuzzy set hyperline segments. The fuzzy set hyperline segment is an n-dimensional hyperline segment defined by two end points with a corresponding membership function. The modification of intersection test in the MFHLSCNN has resulted in improved performance. The thyroid dataset is taken from UCI machine learning repository. The results obtained with the proposed approach are compared with the multilayer perceptron (MLP) trained using error backpropagation and FHLSCNN. The experimental results show that performance of the proposed approach is superior as compared to MLP and FHLSCNN. Moreover training time and recall time per pattern of MFHLSCNN and FHLSCNN is very less as compared to MLP.