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An Automated Diagnosis System of Liver Disease using Artificial Immune and Genetic Algorithms

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Abstract

The rise of health care cost is one of the world’s most important problems. Disease prediction is also a vibrant research area. Researchers have approached this problem using various techniques such as support vector machine, artificial neural network, etc. This study typically exploits the immune system’s characteristics of learning and memory to solve the problem of liver disease diagnosis. The proposed system applies a combination of two methods of artificial immune and genetic algorithm to diagnose the liver disease. The system architecture is based on artificial immune system. The learning procedure of system adopts genetic algorithm to interfere the evolution of antibody population. The experiments use two benchmark datasets in our study, which are acquired from the famous UCI machine learning repository. The obtained diagnosis accuracies are very promising with regard to the other diagnosis system in the literatures. These results suggest that this system may be a useful automatic diagnosis tool for liver disease.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61100150, Natural Science Foundation of Guangdong Province of China under Grant No. S2011040004528, No.S2011040004121 and No.S2011040004121.

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Authors and Affiliations

  1. School of Information, Guangdong Ocean University, Zhanjiang, 524088, China

    Chunlin Liang

  2. School of Computer Science and Education, Guangzhou University, Guangzhou, 510006, China

    Lingxi Peng

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  1. Chunlin Liang
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Correspondence to Lingxi Peng.

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Liang, C., Peng, L. An Automated Diagnosis System of Liver Disease using Artificial Immune and Genetic Algorithms. J Med Syst 37, 9932 (2013). https://doi.org/10.1007/s10916-013-9932-9

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  • DOI: https://doi.org/10.1007/s10916-013-9932-9

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