The novel model namely semi-supervised clustering in the fuzzy min–max neural network is proposed. This model is based on fuzzy min–max neural network and semi-supervised learning method. This model is able to consider as a binary classiﬁer in order to determine an input sample affected the liver disease or not. The proposed model is implemented on a real data including 4.156 samples of patients from Gang Thep Hospital and Thai Nguyen National Hospital and four other datasets from UCI. In this method, all input samples are unlabeled samples. Thus, the expense of labeling the data is omitted. This means that the cost of diagnosis progress from collecting data to making decision is low. Experimental results show that the performance of the proposed model on datasets is higher than other compared ones. Keywords Fuzzy min–max neural network · Semi-supervised learning · Additional information · Liver enzyme test · Liver cancer diagnosis 1 Introduction dynamic data of patients. The consultants from physicians or doctors were not included in these decisions. Many scientists and doctors cared about the detection, the The fuzzy rule-based expert system was used by Agrawal treatment, and the diagnosis of all factors related to liver et al. 
Arabian Journal for Science and Engineering – Springer Journals
Published: Jun 5, 2018
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