Integrated Fuzzy Cognitive Map and Chaotic Particle Swarm Optimization for Risk Assessment of Ischemic Stroke

Authors

  • Bhanu Sekhar OBBU NIT srinagar
  • Zamrooda JABEEN

DOI:

https://doi.org/10.22399/ijcesen.540

Keywords:

Soft Computing, Swarm Intelligence, Particle Swarm Optimization, Fuzzy Cognitive Maps

Abstract

Stroke diagnosis is an incredibly difficult process since it involves the interaction of both controllable and uncontrollable factors. The diagnosis of stroke is significantly influenced by these factors, which include a variety of factors such as age, blood pressure, gender, obesity, diabetes, smoking, and heart disease, amongst others. It is vital to develop an intelligent system that enables treatment to be administered in a timely and effective manner. This study discusses the application of the soft computing approach, more specifically fuzzy cognitive mapping (FCM), for the goal of estimating the possibility of patients suffering from an ischemic stroke. The chaotic particle swarm optimization technique has been utilized for the purpose of training the FCM training system. The consideration the opinions that were provided by neurologists in order to ascertain the risk rate that was associated with each individual. In order to a cross-validation with tenfold overlap was utilized. The results obtained from this method were compared to those obtained by support vector machine (SVM) and K-nearest neighbour computations, which were performed on 110 real-world observations. The proposed method demonstrated an exceptional level of performance, as seen by its overall accuracy of 94.6 percent and its standard deviation of 3.1 percent.

References

. B. Kosko, (1986). Fuzzy cognitive maps, Int. J. Man-Mach. Stud. 24(1);65–75 https://doi.org/10.1016/S0020-7373(86)80040-2

. Axelrod, R. (1976). Structure of Decision: the Cognitive Maps of Political Elites. Princeton, NJ: Princeton University Press

. N. de Almeida Levino, V.B. Schramm, F. Schramm, (2018). The use of fuzzy cognitive maps to support problem structuring in watershed committee, in: Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC, Miyazaki, Japan, pp. 3112–3116. 10.1109/SMC.2018.00527

. G. Mazzuto, M. Bevilacqua, C. Stylios, V.C. Georgopoulos, (2018). Aggregate experts knowledge in fuzzy cognitive maps, in: Proceedings of the 2018 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE, Rio de Janeiro, pp. 1–6 10.1109/FUZZ-IEEE.2018.8491656

. D.G. Cataño, M.S. Arbeláez, A. and Peña, (2019). Fuzzy cognitive maps to evaluate the influence of the infants about home buying decisions, in: Proceedings of the 2019 Iberian Conference on Information Systems and Technologies, CISTI, Coimbra, Portugal, pp. 1–6. 10.23919/CISTI.2019.8760887

. Á. Garzón Casado, P. Cano Marchal, J. Gómez Ortega, J. Gámez García, (2019). Visualization and interpretation tool for expert systems based on fuzzy cognitive maps, IEEE Access 7;6140–6150. 10.1109/ACCESS.2018.2887355

. X. Wei, X. Luo, Q. Li, J. Zhang, Z. Xu, (2015). Online comment-based hotel quality automatic assessment using improved fuzzy comprehensive evaluation and fuzzy cognitive map, IEEE Trans. Fuzzy Syst. 23(1);72–84.

. J. Liu, Y. Chi, Z. Liu, S. He, (2019). Ensemble multi-objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps, CAAI Trans. Intell. Technol. 4 (1);24–36 10.1049/trit.2018.1059

. D. Cavaliere, S. Senatore, V. Loia (2019), Proactive UAVs for cognitive contextual awareness, IEEE Syst. J. 13(3);3568–3579. 10.1109/JSYST.2018.2817191

. P. Groumpos, (2020). A new mathematical modell for COVID-19: A fuzzy cognitive map approach for coronavirus diseases, in: Proceedings of the 2020 11th International Conference on Information, Intelligence, Systems and Applications, IISA, Piraeus,pp.1–6. 10.1109/IISA50023.2020.9284378

. Papageorgiou, E.I., Spyridonos, P., Ravazoula, P., Stylios, C.D., Groumpos, P.P., and Nikiforidis, G. (2004b). Grading Urinary Bladder Tumors Using Unsupervised Hebbian Algorithm for Fuzzy Cognitive Maps. Biomedical Soft Computing and Human Sciences, 9(2);33–39.

. Papageorgiou, E.I., Spyridonos, P., Stylios, C.D., Nikiforidis, G., and Groumpos, P.P. (2004c). The Challenge of Using Soft Computing Techniques for Tumor Characterization. Lecture Notes in Computer Science (LNAI), 3070, 1031–1036. 10.1007/978-3-540-24844-6_161

. Koulouriotis, D.E., Diakoulakis, I.E., and Emiris, D.M. (2001). Learning Fuzzy Cognitive Maps Using Evolution Strategies: A Novel Schema For Modeling and Simulating High–Level Behavior. In Proc. 2001 IEEE Cong. Evol. Comp. Seoul, Korea. 10.1109/CEC.2001.934413

. K. E. Parsopoulos, E. I. Papageorgiou, P. P. Groumpos, and M. N. Vrahatis, (2003). A first study of fuzzy cognitive maps learning using particle swarm optimization, in Proc. IEEE Congr. Evol. Comput., pp. 1440–1447. 10.1109/CEC.2003.1299840

. Petalas, Y., Papageorgiou, E., Parsopoulos, K., Groumpos, P., Vrahatis, M.: (2005). Fuzzy cognitive maps learning using memetic algorithms. In: Proceedings of the international conference of Computational Methods in Sciences and Engineering(ICCMSE 2005), pp. 1420-1423

. Petalas, Y.G., Parsopoulos, K.E., Vrahatis, M.N.: (2009). Improving fuzzy cognitive maps learn- ing through memetic particle swarm optimization. Soft Computing 13(1);77-94. 10.1007/s00500-008-0311-2

. Hosna Nasiriyan-Rad, Abdollah Amirkhani, Azar Naimi, Karim Mohammadi, (2016). Learning fuzzy cognitive map with PSO algorithm for grading celiac disease, 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME). 10.1109/ICBME.2016.7890984

. M. S. Khan and A. Chong, (2003). Fuzzy cognitive map analysis with genetic algorithm,” presented at the Ind. Int. Conf. Artif. Intell., Hyderabad, India.

. Mateou, N.H., Moiseos, M., Andreou, A.S.: (2005). Multi-objective evolutionary fuzzy cognitive maps for decision support. In: Proceedings of the 2005 Congress on Evolutionary Computation, 1;824-830. IEEE 10.1109/CEC.2005.1554768

. Stach, W., Kurgan, L., Pedrycz, W.: (2010). A divide and conquer method for learning large fuzzy cognitive maps. Fuzzy Sets and Systems 161(19); 2515-2532. 10.1016/j.fss.2010.04.008

. Stach, W., Kurgan, L., Pedrycz, W.: (2007). Parallel learning of large fuzzy cognitive maps. In: International Joint Conference on Neural Networks, pp. 1584-1589.

. Jing Liu, Yaxiong Chi, Zongdong Liu, Shan He, (2019). Ensemble multi-objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps, CAAI Trans. Intell. Technol., 4(1);24–36, 2019. DOI: 10.1049/trit.2018.105

. Obbu, B. S., & Jabeen, Z. (2023). Study of convergence speed of chaotic particle swarm optimization algorithm. Pollack Periodica (published online ahead of print 2023). 10.1556/606.2023.00933

. Faraji, F., Ranjbar, A., Eshrati, B., et al.: (2008). Comparing the oxidative stress indexes of CVA patients with control group’, J. Arak Univ. Med. Sci., 2008, 11(3);109–116

. Zweifler, R.M.: (2017). Initial assessment and triage of the stroke patient’, Program. Cardiavasc. Dis., 59(6);527–533 10.1016/j.pcad.2017.04.004

. Deb, P., Sharma, S., Hassan, K.: (2010). Pathophysiologic mechanisms of acute ischemic stroke: an overview with emphasis on therapeutic significance beyond thrombolysis, Pathophysiology, 17(3);197–218. 10.1016/j.pathophys.2009.12.001

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Published

2024-10-31

How to Cite

Bhanu Sekhar OBBU, & Zamrooda JABEEN. (2024). Integrated Fuzzy Cognitive Map and Chaotic Particle Swarm Optimization for Risk Assessment of Ischemic Stroke. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.540

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Section

Research Article