Abstract
In the growing scenario, microarray data is extensively used since it provides a more comprehensive understanding of genetic variants among diseases. As the gene expression samples have high dimensionality it becomes tedious to analyze the samples manually. Hence an automated system is needed to analyze these samples. The fuzzy expert system offers a clear classification when compared to the machine learning and statistical methodologies. In fuzzy classification, knowledge acquisition would be a major concern. Despite several existing approaches for knowledge acquisition much effort is necessary to enhance the learning process. This paper proposes an innovative Hybrid Stem Cell (HSC) algorithm that utilizes Ant Colony optimization and Stem Cell algorithm for designing fuzzy classification system to extract the informative rules to form the membership functions from the microarray dataset. The HSC algorithm uses a novel Adaptive Stem Cell Optimization (ASCO) to improve the points of membership function and Ant Colony Optimization to produce the near optimum rule set. In order to extract the most informative genes from the large microarray dataset a method called Mutual Information is used. The performance results of the proposed technique evaluated using the five microarray datasets are simulated. These results prove that the proposed Hybrid Stem Cell (HSC) algorithm produces a precise fuzzy system than the existing methodologies.
Similar content being viewed by others
References
Pomero, F., Di Minno, M. N. D., Fenoglio, L., Gianni, M., Ageno, W., and Dentali, F., Is diabetes a hypercoagulable state? A critical appraisal. Acta Diabetol. 52(6):1007-1016, 2015.
Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P., and Li, G., An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst. Appl. 68:163-172, 2017.
Diz, J., Marreiros, G., and Freitas, A., Applying data mining techniques to improve breast cancer diagnosis. J. Med. Syst. 40(9):203, 2016.
Chang, X., and Yang Y., Semisupervised feature analysis by mining correlations among multiple tasks." IEEE transactions on neural networks and learning systems 28(10):2294-2305, 2017.
Wei Liang, Mingdong Tang, Long Jing, Arun Kumar Sangaiah, Yin Huang, (2018) SIRSE: A secure identity recognition scheme based on electroencephalogram data with multi-factor feature. Computers & Electrical Engineering 65:310-321
Zhang, R., Shen J., Wei F., Li X., and Sangaiah A. K., Medical image classification based on multi-scale non-negative sparse coding. Artificial intelligence in medicine 83:44-51, 2017.
Yoon, Y., Bien, S., and Park, S., Microarray data classifier consisting of k-top-scoring rank-comparison decision rules with a variable number of genes. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(2):216–226, 2010.
Camara, C., Warwick, K., Bruña, R., Aziz, T., Del Pozo, F., and Maestú, F., A fuzzy inference system for closed-loop deep brain stimulation in Parkinson’s disease. J. Med. Syst. 39(11):155, 2015.
Vinterbo, S. A., Kim, E. Y., and Ohno-Machado, L., Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics, 21(9):1964-1970, 2005.
Wang, Z., and Palade, V., A comprehensive fuzzy-based framework for cancer microarray data gene expression analysis. In BIBE 2007. Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, 2007. (pp. 1003-1010). IEEE, 2007.
Chen, S. M., and Tsai, F. M., Generating fuzzy rules from training instances for fuzzy classification systems. Expert Syst. Appl. 35(3):611-621, 2008.
Schaefer, G., and Nakashima, T., Data mining of gene expression data by fuzzy and hybrid fuzzy methods. IEEE Trans. Inf. Technol. Biomed. 14(1):23–29, 2010.
Kumar, P. G., Victoire, T. A. A., Renukadevi, P., and Devaraj, D., Design of fuzzy expert system for microarray data classification using a novel genetic swarm algorithm. Expert Syst. Appl., 39(2):1811-1821, 2012.
Chang, X., Nie, F., Yang, Y., and Huang, H., A convex formulation for semi-supervised multi-label feature selection. In AAAI, pp. 1171-1177, 2014, July.
Chen, H. L., Yang, B., Wang, G., Wang, S. J., Liu, J., and Liu, D. Y., Support vector machine based diagnostic system for breast cancer using swarm intelligence. J. Med. Syst. 36(4):2505-2519, 2012.
Dorigo, M., and Stutzle, T., Ant Colony Optimization‖. MIT Press, Cambridge, MA, 2004.
Lin, K. C., and Hsieh, Y. H., Classification of medical datasets using SVMs with hybrid evolutionary algorithms based on endocrine-based particle swarm optimization and artificial bee colony algorithms. J. Med. Syst. 39(10):119, 2015.
Kumar, P. G., Vijay, S. A. A., and Devaraj, D., A hybrid colony fuzzy system for analyzing diabetes microarray data. In computational intelligence in bioinformatics and computational biology (CIBCB), 2013 I.E. Symposium on (pp. 104-111). IEEE, 2013, April.
Devaraj, D., Roselyn, J. P., and Rani, R. U., Artificial neural network model for voltage security based contingency ranking. Appl. Soft Comput. 7(3):722-727, 2007.
Mak, D. K., A fuzzy probabilistic method for medical diagnosis. J. Med. Syst. 39(3):26, 2015.
Pulkkinen, P., and Koivisto, H., Identification of interpretable and accurate fuzzy classifiers and function estimators with hybrid methods. Appl. Soft Comput. 7(2):520–533, 2007.
Samuel, O. W., Zhou, H., Li, X., Wang, H., Zhang, H., Sangaiah, A. K., and Li, G., Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification. Comput. Electr. Eng. 2017.
Liao, X., Yin, J., Guo, S., Li, X., and Sangaiah, A. K., Medical JPEG image steganography based on preserving inter-block dependencies. Comput. Electr. Eng. 2017.
Dashtban, M., and Balafar, M., Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics 109(2)91-107, 2017.
Lazar, C., Taminau, J., Meganck, S., Steenhoff, D., Coletta, A., Molter, C., de Schaetzen, V., Duque, R., Bersini, H., and Nowe, A., A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(4):1106–1119, 2012.
Wang, J., Wang, H., Zhou, Y., and McDonald, N., Multiple kernel multivariate performance learning using cutting plane algorithm. In 2015 I.E. international conference on Systems, man, and cybernetics (SMC), (pp. 1870-1875). IEEE, 2015, October.
Ganesh Kumar, P., and Aruldoss Albert Victoire, T., Multistage mutual information for informative gene selection. J. Biol. Syst. 19(04):725-746, 2011.
Taherdangkoo, M., Yazdi, M., and Bagheri, M. H., Stem cells optimization algorithm. In International Conference on Intelligent Computing (pp. 394-403). Springer, Berlin, Heidelberg, 2011, August.
Wang, H., and Wang, J., An effective image representation method using kernel classification. In 2014 I.E. 26th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 853-858). IEEE, 2014, November.
Author information
Authors and Affiliations
Corresponding author
Additional information
This article is part of the Topical Collection on Systems-level Quality Improvement
Rights and permissions
About this article
Cite this article
Vijay, S.A.A., GaneshKumar, P. Fuzzy Expert System based on a Novel Hybrid Stem Cell (HSC) Algorithm for Classification of Micro Array Data. J Med Syst 42, 61 (2018). https://doi.org/10.1007/s10916-018-0910-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-018-0910-0