Takagi-Sugeno-Kang(zero-order) model for diagnosis hepatitis disease


  • Raidah S. Salim Basra University




medical diagnosis, Fuzzy logic, fuzzy neural network, Microarray, Attribute Reduction Scheme, Mean Imputation.


The aim of this paper is to use Takagi-Sugeno-Kang(zero-order) model as fuzzy neural network for the medical diagnosis of hepatitis diseases which represent a major public health problem all around the world . For further improve the accuracy and the speed of the diagnosis, the Microarray Attribute Reduction Scheme (MARS) for reduction features (or attributes) and Mean Imputation (MI) method for treatment the missing values were used in this work. The used data source of hepatitis diseases was taken from UCI machine learning repository. After treat the missing values problem by apply MI method, the dataset is partitioned into three training–testing partitions (30%–70%, 40–60% and 20%–80% respectively) and apply MARS with different values of thr(from 0.1-0.9 ) in order to determine the number attributes (that represent the number of inputs to the fuzzy neural network), the results record in each case of thr values and each case of partitions. The high diagnosis accuracy has been achieved for the 40–60% training–testing, namely, 100% for training and 95.77% for testing with thr equal to 0.4 and with less training cycle and fuzzy sets number. This work was implemented in MATLAB 7.0 environment. Keywords: medical diagnosis, Fuzzy logic, fuzzy neural network, Microarray Attribute Reduction Scheme, Mean Imputation.


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How to Cite

Salim, R. S. (2015). Takagi-Sugeno-Kang(zero-order) model for diagnosis hepatitis disease. Journal of Kufa for Mathematics and Computer, 2(3), 73–84. https://doi.org/10.31642/JoKMC/2018/020307

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