Journal of Digital Information Management


Vol No. 18 ,Issue No. 5 2020

A New Approach Based Symbiotic Organism Search using Data Mining for Medical Decision
Samia Nouredine, Abida Toumi, Baarir Zineeddine
Electrical Engineering Department, University of Biskra
Abstract: In the medical field the decision represents an extremely important asset because the risk must be zero. This is why decision approaches which are based on a predictive vision are a must solution. The decision is generally based on the exploitation of a large volume of medical data. The processing and analysis of mass data is only possible through an extraction of knowledge allowing the medical experts to make the best decision. Thus to meet this need, data mining has become the most promising approach. There are several techniques of datamining, and although they are quite developed the y still remain even less efficient notably the classical meta-heuristics. In this paper, we are exploiting a new meta-heuristic called symbiotic organisms search (SOS) that is based on a biological process. In this paper, we develop the formal model of the SOS based data mining process in the medical field with a comparative study with other metaheuristics to show its performance and credibility of treatment.
Keywords: Symbiotic, Symbiosis, Datamining, Metaheuristic, Optimization, Classification A New Approach Based Symbiotic Organism Search using Data Mining for Medical Decision
DOI:https://doi.org/10.6025/jdim/2020/18/5-6/195-209
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References:

[1] Douiri, Sidi Mohamed., Elbernoussi, Souad., Lakhbab, Halima. (2012). Course of Exact Methods of Heuristic Heuristics and Metaheuristics, University Mohammed V, Faculty of Sciences of Rabat.
[2] Lateb, Abdesslem. (2010). Using Combinatorial Optimization Approaches for Real-Time Application Verification. Ph.D. thesis, Mentouri University of Constantine 2010. Algeria.
[3] Freitas A. A. (2008). A Review of evolutionary Algorithms for Data Mining, In: Maimon O., Rokach L. (eds) Soft Computing for Knowledge Discovery and Data Mining. Springer, Boston, MA, p. 371-400.
[4] Wu, Haizhou. (2016). Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm, Hindawi Publishing Corporation, Computational Intelligence and Neuroscience, Article ID 9063065, 14 pages.
[5] Govender, Prinolan., Ezugwu, Absalom. (2018). A symbiotic organisms search algorithm for optimal allocation of blood products. IEEE Access. 7. p. 2567-2588. 10.1109/ACCESS.2018.2886408.
[6] Govender, P., Ezugwu, A. E. (2019). A Symbiotic Organisms Search Algorithm for Blood Assignment Problem. In: Blesa Aguilera M., Blum C., Gambini Santos H., Pinacho-Davidson P., Godoy del Campo J. (eds) Hybrid Metaheuristics. HM 2019. Lecture Notes in Computer Science, vol 11299. Springer, Cham.
[7] Karaboga, D., Akay, B. (2017). Artificial Bee Colony (ABC), Harmony Search and Bees Algorithms on Numerical Optimization, Erciyes University, The Dept. of Computer Engineering, 38039, Melikgazi, Kayseri, Turkiye.
[8] Reynolds, G., Peng, Bin. (2005). Cultural Algorithms Computational Modeling Of How Cultures Learn To Solve Problems: An Engineering Example, Cybernetics and Systems 36 (8) 753-771, (December).
[9] Mirjalili, S., Mirjalili, S. M., Lewis, A. (2014). Grey Wolf Optimizer, Advances in Engineering Software, vol. 69, p. 46-61.
[10] Chen, Ke., Zhou, Fengyu., Liu, Aling. (2018). Chaotic Dynamic Weight Particle Swarm Optimization for Numerical Function Optimization, Knowledge-Based Systems, 139 (C), (January), p. 23-40.
[11] John. H., Holland. (1992). Adaptation in Natural and Artificial Systems, An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, Bradford Book, The University of Michigan, Editor MIT press.
[12] Kanan, Hamidreza Rashdy., Nazeri, Bahram. (2019).
A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm, Expery Systems with Application, 41, p. 546-558.
[13] Karol, R., Opara, Jaroslaw Arabas. (2019). Differential Evolution: A survey of theoretical analyses, Swarm and Evolutionary Computation, 44, p. 546-558, (February). https://doi.org/10.1016/j.swevo.2018.06.010
[14] Langdon, W. B., Poli, R. (2007). Evolving problems to learn about particle optimizers and other search algorithms, IEEE Transactions on Evolutionary Conputation, 11 (5), p. 561-578.
[15] Cheng, Min-Yuan., Prayogo, Doddy. (2016). Symbiotic Organisms Search: A new metaheuristic optimization algorithm, Computers and Structures, 139, 15 July 2014, p. 98-112.
[16] Cheng, Min-Yuan., Prayogo, Doddy., Duc-Hoc Tran. (2016). Optimizing multiple-resources leveling in multiple projects using discrete symbiotic organisms search, Journal of Computing in Civil Engineering, 30 (3) - May 2016, p. 1-9.
[17] Ke, Chen., Zhou, Fengyu., Liu, Aling. (2017). Chaotic Dynamic Weight Particle Swarm Optimization for Numerical Function Optimization, Knowledge-Based Systems, p. 23-40.
[18] Lichman, M. (2016). UCI Machine Learning Repository; University of California, School of Information and Computer Science: Irvine, CA, USA, 2013; Available online: http://archive.ics.uci.edu/ml (accessed on 13 March 2016).
[19] Krzywinski, M., Altman, N. (2014). Points of significance: visualizing samples with box plots, Nat Methods 11, p. 119-120.
[20] El Helou, Georges., Abou khalil, Charbel. (2004). Data Mining Knowledge Extraction Techniques, Models of the Digital Economy, Project supported, University Paris II, (February 16).
[21] Hand, J., Kamber, M. (2006). Data Mining: Concepts and Techniques, Second edition, Editor Elsevier, USA, p. 1-32.
[22] Kalami Heris, Moustapha., S. Project Title: Evolutionary
Clustering and Automatic Clustering, Khaje Nasir Toosi University Tehran, Iran.
[23] Noureddine, Samia., Toumi, Abida., Betka, Abir. (2018). Symbiotic Approach for Datamining, ICCES’18 the 4th ACM International Conference of Computing for Engineering and Sciences, Kuala Lumpur, Malaysia — July 06 - 08, (2018), ISBN: 978-1-4503-6447-8, doi:10.1145/3213187.3287610.
[24] https://docs.microsoft.com/fr-fr/sql/analysis-services/data-mining/classification-matrix-analysis-services-datamining?view=sql-analysis-services-2017
[25] Kummar, R., Indrawn, A. (2011). Receiver OperatingCharacteristic (ROC) Curve for Medical Researchers, Indian Pediatrics, vol. 48, p. 277-289.