Abstract
In this paper, we designed a hybrid of swarm intelligence algorithms to diagnose hepatitis, breast tissue, and dermatology conditions in patients with such infection. The effectiveness of hybrid swarm intelligent algorithms was studied since no single algorithm is effective in solving all types of problems. In this study, feed forward and Elman recurrent neural network (ERN) with swarm intelligent algorithms is used for the classification of the mentioned diseases. The capabilities of six (6) global optimization learning algorithms were studied and their performances in training as well as testing were compared. These algorithms include: hybrid of Cuckoo Search algorithm and Levenberg-Marquardt (LM) (CSLM), Cuckoo Search algorithm (CS) and backpropagation (BP) (CSBP), CS and ERN (CSERN), Artificial Bee Colony (ABC) and LM (ABCLM), ABC and BP (ABCBP), Genetic Algorithm (GA) and BP (GANN). Simulation comparative results indicated that the classification accuracy and run time of the CSLM outperform the CSERN, GANN, ABCBP, ABCLM, and CSBP in the breast tissue dataset. On the other hand, the CSERN performs better than the CSLM, GANN, ABCBP, ABCLM, and CSBP in both the dermatology and hepatitis datasets. The results obtained could allow researchers to quickly identify the most suitable algorithm for application in a particular medical dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ture, M., Kurt, I.: Comparison of four different time series methods to forecast hepatitis A virus infection. Expert Syst. Appl. 31(1), 41–46 (2006)
Bickers, D.R., Henry, W.L., David, M., Martin, A., Weinstock, C.G., Eric, F., Ciara, G., Eric, G., Tim, D.: The burden of skin diseases: 2004: a joint project of the american academy of dermatology association and the society for investigative dermatology. J. Am. Acad. Dermatol. 55(3), 490–500 (2006)
Lanigan, F., O’Connor, D., Martin, F., Gallagher, W.M.: Molecular links between mammary gland development and breast cancer. Cell. Mol. Life Sci. 64(24), 3159–3184 (2007)
Lanza, R., Langer, R., Vacanti, J.P.: Principles of Tissue Engineering. Academic press, USA (2011)
Xu, J., Sun, L., Gao, Y., Xu, T.: An ensemble feature selection technique for cancer recognition. Bio-Med. Mater. Eng. 24(1), 1001–1008 (2014)
Jie, X., Cao, R., Li, L.: Emotion recognition based on the sample entropy of EEG. Bio-Med. Mater. Eng. 24(1), 1185–1192 (2014)
Chen, K., Li, B., Tian, L.F., Zhu, W.B., Bao, Y.H.: Fuzzy speed function based active contour model for segmentation of pulmonary nodules. Bio-Med. Mater. Eng. 24(1), 539–547 (2014)
Zhang, E., Wang, F., Li, Y., Bai, X.: Automatic detection of microcalcifications using mathematical morphology and a support vector machine. Bio-Med. Mater. Eng. 24(1), 53–59 (2014)
Tong, D.L., Schierz, A.C.: Hybrid genetic algorithm-neural network: feature extraction for unpreprocessed microarray data. Artif. Intell. Med. 53(1), 47–56 (2011)
Yang, X.S., Deb, S.: Cuckoo search: recent advances and applications. Neural Comput. Appl. 24(1), 169–174 (2014)
Haykin, S.: Neural Network: A Comprehensive Foundation. Pearson Education, New Delhi (2005)
Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14, 35–62 (1998)
Elman, J.L.: Finding structure in time. Cognitive Science 14, 179–211 (1990)
Gencay, R., Liu, T.: Nonlinear modeling and prediction with feedforward and recurrent networks. Physica D 108, 119–134 (1997)
Haykin, S.: Neural networks, 2nd edn. Prentice Hall, New Jersey (1999)
Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 1, 330–343 (2010)
Karaboga, D., Akay, B., Ozturk, C.: Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In: Modeling Decisions for Artificial Intelligence, pp. 318–329. Springer, Berlin, Heidelberg (2007)
Kaynar, O., Yilmaz, I., Demirkoparan, F.: Forecasting of natural gas consumption with neural networks and neuro fuzzy system. Energy Educ. Sci. Technol. 26(2), 221–238 (2011)
Chiroma, H., Abdulkareem, S., Herawan, T.: Evolutionary neural network model for West Texas intermediate crude oil price prediction. Appl. Energy 142, 266–273 (2015)
Walton, S., Hassan, O., Morgan, K., Brown, M.R.: Modified cuckoo search: a new gradient free optimization algorithms. Chaos, Solitons Fractals 44(9), 710–718 (2011)
Chiroma, H., Abdulkareem, S., Muaz, S.A., Abubakar, A., Herawan, T.: An intelligent modeling of oil consumption. In: El-Alfy, E.-S., Thampi, S.M., Takagi, H., Piramuthu, S., Hanne, T. (eds.) Advances in Intelligent Informatics, pp. 557–568 (2015)
Acknowledgements
This work is supported by University of Malaya High Impact Research Grant no vote UM.C/625/HIR/MOHE/SC/13/2 from Ministry of Higher Education Malaysia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chiroma, H. et al. (2019). Hybrid of Swarm Intelligent Algorithms in Medical Applications. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_63
Download citation
DOI: https://doi.org/10.1007/978-981-13-1799-6_63
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1797-2
Online ISBN: 978-981-13-1799-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)