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

• Published in: Journal of medical systems Vol. 37; no. 2; pp. 9932 - 10
• Main Authors: Liang, Chunlin, Peng, Lingxi
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.04.2013; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Automation; Benchmarking; Classification; Cluster analysis; Computational Biology; Computer Simulation; Decision making; Diagnosis; Diagnosis, Computer-Assisted; Evolution; Genetic algorithms; Genetics; Health Informatics; Health Sciences; Hepatitis; Humans; Immune System; Immune systems; Intelligent systems; Learning; Liver; Liver diseases; Liver Diseases - diagnosis; Liver Diseases - immunology; Machine learning; Medical diagnosis; Medical research; Medicine; Medicine & Public Health; Methods; Neural networks; Original Paper; Reproducibility of Results; Statistics for Life Sciences; Support Vector Machine; Support vector machines; Artificial immune; Genetic algorithm; Liver disease diagnosis; Machine learning
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-013-9932-9

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.