Healthcare Data Analysis Using Water Wave Optimization-Based Diagnostic Model
DOI:
https://doi.org/10.32890/jict2021.20.4.1Keywords:
Computational Intelligence, Water Wave Optimization, Disease Diagnosis, Diagnostic Model, Meta-heuristic techniquesAbstract
This paper presents a new diagnostic model for various diseases. In the proposed diagnostic model, a water wave optimization (WWO)
algorithm was implemented for improving the diagnosis accuracy. It was observed that the WWO algorithm suffered from the absence
of global best information and premature convergence problems. Therefore in this work, some improvements were proposed to formulate the WWO algorithm as more promising and efficient. The global best information issue was addressed by using an improved
solution search equation and the aim of this was to explore the global best optimal solution. Furthermore, a premature convergence problem was rectified by using a decay operator. These improvements were incorporated in the propagation and refraction phases of the WWO algorithm. The proposed algorithm was integrated into a diagnostic model for the analysis of healthcare data. The proposed algorithm aimed to improve the diagnosis accuracy of various diseases. The diverse disease datasets were considered for implementing the performance of the proposed diagnostic model based on accuracy and F-score performance indicators, while the existing techniques were regarded to compare the simulation results. The results confirmed that the WWO-based diagnostic model achieved a higher accuracy rate as compared to existing models/techniques with most disease/healthcare datasets. Therefore, it stated that the proposed diagnostic model is more promising and efficient for the diagnosis of different diseases.
References
Al-Muhaideb, S., & Menai, M. E. B. (2013). Hybrid metaheuristics for medical data classification. Hybrid Metaheuristics (pp. 187-217). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30671-6_7
Alkeshuosh, A. H., Moghadam, M. Z., Al Mansoori, I., & Abdar, M. (2017, September). Using PSO algorithm for producing best rules in diagnosis of heart disease. In IEEE International Conference on Computer and Applications (ICCA) (pp. 306–311). https://doi.org/10.1109/comapp.2017.8079784 Journal of ICT, 20, No. 4 (October) 2021, pp: 457–
Alsayat, A., & El-Sayed, H. (2016, June). Efficient genetic K-means clustering for health care knowledge discovery. In 14th IEEE International Conference on Software Engineering Research, Management and Applications (SERA) (pp. 45–52). https://doi.org/10.1109/sera.2016.7516127
Altayeva, A., Zharas, S., & Cho, Y. (2016, October). Medical decision-making diagnosis system integrating k-means and Naïve Bayes algorithms. In 16th IEEE International Conference on Control, Automation and Systems (ICCAS) (pp. 1087–1092). https://doi.org/10.1109/iccas.2016.7832446
Andreopoulos, B., An, A., Wang, X., & Schroeder, M. (2009). A roadmap of clustering algorithms: Finding a match for a biomedical application. Briefings in Bioinformatics, 10(3), 297–314. https://doi.org/10.1093/bib/bbn058
Baek, J. W., Kim, J. C., Chun, J., & Chung, K. (2019). Hybrid clustering-based health decision-making for improving dietary habits. Technology and Health Care, 27(5), 1–14. https://doi.org/10.3233/thc-191730
Bekaddour, F., & Chikhi, S. (2016, November). A comparative study of metaheuristics for liver disorders prediction. In Proceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence (pp. 1–6). https://doi.org/10.1145/3038884.3038885
Belciug, S., & Gorunescu, F. (2020). Era of intelligent systems in healthcare. In Intelligent Decision Support Systems—A Journey to Smarter Healthcare (pp. 1–55). https://doi.org/10.1007/978-3-030-14354-1_1
Bezdek, J. C. (1994). What is computational intelligence? In J. M. Zurada, R. J. Marks II, & C. J. Robinson (Eds.), Computational intelligence: Imitating life (pp. 1–12). IEEE Press. https://www.researchgate.net/publication/220045330_What_is_ Computational_Intelligence
Bezdek, J. C. (1998). Computational intelligence defined-by everyone! In Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications (pp. 10–37). Springer. https://doi.org/10.1007/978-3-642-58930-0_2 Journal of ICT, 20, No. 4 (October) 2021, pp: 457–
Buhmann, J. (1995). Data clustering and learning. The Handbook of Brain Theory and Neural Networks, 278–281. https://ml2.inf. ethz.ch/papers/2002/buhmann.mitpress02.pdf
Devikanniga, D. (2020). Diagnosis of osteoporosis using intelligence of optimized extreme learning machine with improved artificial algae algorithm. International Journal of Intelligent Networks, 1, 43–51. https://doi.org/10.1016/j.ijin.2020.05.004
Duch, W. (2007). What is computational intelligence and where is it going? In Challenges for Computational Intelligence (pp. 1–13). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71984-7_1
Engelbrecht, A. P. (2007). Computational intelligence: An introduction. John Wiley & Sons. ISBN: 978-0-470-03561-0.
Gadekallu, T. R., & Khare, N. (2017). Cuckoo search optimized reduction and fuzzy logic classifier for heart disease and diabetes prediction. International Journal of Fuzzy System Applications (IJFSA), 6(2), 25–42. https://doi.org/10.4018/ijfsa.2017040102
Hematabadi, A. A., & Foroud, A. A. (2019). Optimizing the multi-objective bidding strategy using min–max technique and modified water wave optimization method. Neural Computing and Applications, 31(9), 5207–5225. https://doi.org/10.1007/ s00521-018-3361-0
Ibrahim, A. M., Tawhid, M. A., & Ward, R. K. (2020). A binary water wave optimization for feature selection. International Journal of Approximate Reasoning, 120, 74–91. https://doi.org/10.1016/j.ijar.2020.01.012
Jothi, N., & Husain, W. (2015). Data mining in healthcare – A review. Procedia Computer Science, 72, 306–313. https://doi.org/10.1016/j.procs.2015.12.145
Khan, M. A., & Algarni, F. (2020). A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE Access, 8, 122259–122269. https://doi: 10.1109/ACCESS.2020.3006424
Khanmohammadi, S., Adibeig, N., & Shanehbandy, S. (2017). An improved overlapping k-means clustering method for medical applications. Expert Systems with Applications, 67, 12–18. https://doi.org/10.1016/j.eswa.2016.09.025 Journal of ICT, 20, No. 4 (October) 2021, pp: 457–
Kuo, R. J., Lin, S. Y., & Shih, C. W. (2007). Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan. Expert Systems with Applications, 33(3), 794–808. https://doi.org/10.1016/j. eswa.2006.08.035
Kushwaha, N., & Pant, M. (2018). Fuzzy magnetic optimization clustering algorithm with its application to health care. Journal of Ambient Intelligence and Humanized Computing, 1–10. https://doi.org/10.1007/s12652-018-0941-x
Le Minh, T., Minh, T. V., Pham, T. N., & Dao, S. V. T. (2020). A novel wrapper–based feature selection for early diabetes prediction enhanced with a metaheuristic. IEEE Access, 9, 7869–7884. https://doi.org/10.1109/access.2020.3047942
Lenin, K., Reddy R. B., & Suryakalavathi, M. (2016). Hybridization of firefly and water wave algorithm for solving reactive power problem. International Journal of Engineering Research in Africa, 21, 165–171. https://doi.org/10.4028/www.scientific. net/JERA.21.165
Liu, A., Li, P., Sun, W., Deng, X., Li, W., Zhao, Y., & Liu, B. (2019). Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization. Neural Computing and Applications, 1–16. https://doi.org/10.1007/ s00521-019-04149-1
Mahendru, S., & Agarwal, S. (2019). Feature selection using metaheuristic algorithms on medical datasets. In Harmony Search and Nature Inspired Optimization Algorithms (pp. 923–937). Springer. https://doi.org/10.1007/978-981-13-0761-4_87
Manogaran, G., Vijayakumar, V., Varatharajan, R., Kumar, P. M., Sundarasekar, R., & Hsu, C. H. (2018). Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering. Wireless Personal Communications, 102(3), 2099–2116. https://doi.org/10.1007/ s11277-017-5044-z
Manshahia, M. S. (2017). Water wave optimization algorithm-based congestion control and quality of service improvement in wireless sensor networks. Transactions on Networks and Communications, 5(4), 31–39. https://doi.org/10.14738/ tnc.54.3567 Journal of ICT, 20, No. 4 (October) 2021, pp: 457–
Ni, J., Fei, H., Fan, W., & Zhang, X. (2017, November). Automated medical diagnosis by ranking clusters across the symptom-disease network. In IEEE International Conference on Data Mining (pp. 1009–1014). IEEE. https://doi.org/10.1109/ icdm.2017.130
Nilashi, M., Bin Ibrahim, O., Mardani, A., Ahani, A., & Jusoh, A. (2018). A soft computing approach for diabetes disease classification. Health Informatics Journal, 24(4), 379–393. https://doi.org/10.1177/1460458216675500
Noureddine, S., Zineeddine, B., Toumi, A., Betka, A., & Benharkat, A. N. (2020). A new predictive medical approach based on data mining and symbiotic organisms search algorithm. International Journal of Computers and Applications, 1–15. https://doi.org/1 0.1080/1206212X.2020.1809825
Nourmohammadi-Khiarak, J., Feizi-Derakhshi, M. R., Behrouzi, K., Mazaheri, S., Zamani-Harghalani, Y., & Tayebi, R. M. (2019). New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection. Health and Technology, 10(1), 1–12. https://doi.org/10.1007/s12553-019-00396-3
Rao, N. M., Kannan, K., Gao, X. Z., & Roy, D. S. (2018). Novel classifiers for intelligent disease diagnosis with multi-objective parameter evolution. Computers & Electrical Engineering, 67, 483–496. https://doi.org/10.1016/j.compeleceng.2018.01.039
Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Rajput, D. S., Kaluri, R., & Srivastava, G. (2020). Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis. Evolutionary Intelligence, 13(2), 185–196. https://doi.org/10.1007/s12065-019-00327-1
Shao, Z., Pi, D., & Shao, W. (2018). A novel discrete water wave optimization algorithm for blocking flow-shop scheduling problem with sequence-dependent setup times. Swarm and Evolutionary Computation, 40, 53–75. https://doi.org/10.1016/j.swevo.2017.12.005s
Shao, Z., Pi, D., & Shao, W. (2019). A novel multi-objective discrete water wave optimization for solving multi-objective blocking flow-shop scheduling problem. Knowledge-Based Systems, 165, 110–131. https://doi.org/10.1016/j.knosys.2018.11.021 Journal of ICT, 20, No. 4 (October) 2021, pp: 457–
Singh, G., Rattan, M., Gill, S. S., & Mittal, N. (2019). Hybridization of water wave optimization and sequential quadratic programming for cognitive radio system. Soft Computing, 23(17), 7991–8011. https://doi.org/10.1007/s00500-018-3437-x
Siva, M., Balamurugan, R., & Lakshminarasimman, L. (2016). Water wave optimization algorithm for solving economic dispatch problems with generator constraints. International Journal of Intelligent Engineering and Systems, 9(4), 31–40. https://doi.org/10.22266/ijies2016.1231.04
Soltanian, A., Derakhshan, F., & Soleimanpour-Moghadam, M. (2018, March). MWWO: Modified water wave optimization. In 2018 3rd Conference on Swarm Intelligence and Evolutionary Computation (pp. 1–5). IEEE. https://doi.org/10.1109/ csiec.2018.8405412
Tsai, C. W., Chiang, M. C., Ksentini, A., & Chen, M. (2016). Metaheuristic algorithms for healthcare: Open issues and challenges. Computers & Electrical Engineering, 53, 421–434. https://doi.org/10.1016/j.compeleceng.2016.03.005
Wang, M., & Chen, H. (2020). Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Applied Soft Computing, 88, 105946. https://doi.org/10.1016/j. asoc.2019.105946
Wu, X. B., Liao, J., & Wang, Z. C. (2015, August). Water Wu, X., Zhou, Y., & Lu, Y. (2017). Elite opposition-based water wave optimization algorithm for global optimization. Mathematical Problems in Engineering, 2017, 1–25. https://doi.org/10.1155/2017/3498363
Zhang, B., Zhang, M. X., Zhang, J. F., & Zheng, Y. J. (2015, August). A water wave optimization algorithm with variable population size and comprehensive learning. In International Conference on Intelligent Computing (pp. 124–136). Springer. https://doi.org/10.1007/978-3-319-22180-9_13
Zhang, J., Zhou, Y., & Luo, Q. (2018). An improved sine cosine water wave optimization algorithm for global optimization. Journal of Intelligent & Fuzzy Systems, 34(4), 2129–2141. https://doi.org/10.3233/JIFS-171001 Journal of ICT, 20, No. 4 (October) 2021, pp: 457–
Zhao, F., Liu, H., Zhang, Y., Ma, W., & Zhang, C. (2018). A discrete water wave optimization algorithm for no-wait flow shop scheduling problem. Expert Systems with Applications, 91, 347–363. https://doi.org/10.1016/j.eswa.2017.09.028
Zhao, F., Zhang, L., Liu, H., Zhang, Y., Ma, W., Zhang, C., & Song, H. (2019a). An improved water wave optimization algorithm with the single wave mechanism for the no-wait flow-shop scheduling problem. Engineering Optimization, 51(10), 1727–1742. https://doi.org/10.1080/0305215X.2018.1542693
Zhao, F., Zhang, L., Zhang, Y., Ma, W., Zhang, C., & Song, H. (2019b). An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning. Connection Science, 1–30. https://doi.org/10.1080/09540091.2019.1674247
Zhao, F., Zhang, L., Zhang, Y., Ma, W., Zhang, C., & Song, H. (2020). A hybrid discrete water wave optimization algorithm for the no-idle flowshop scheduling problem with total tardiness criterion. Expert Systems with Applications, 146, 113166. https://doi.org/10.1016/j.eswa.2019.113166
Zheng, Y. J. (2015). Water wave optimization: A new nature-inspired metaheuristic. Computers & Operations Research, 55, 1–11. https://doi.org/10.1016/j.cor.2014.10.008
Zheng, Y. J., & Zhang, B. (2015, May). A simplified water wave optimization algorithm. In 2015 IEEE Congress on Evolutionary Computation (pp. 807–813). IEEE. https://doi.org/10.1109/ cec.2015.7256974 Zomorodi‐moghadam, M., Abdar, M., Davarzani, Z., Zhou, X., Pławiak, P., & Acharya, U. R. (2021). Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease. Expert Systems, 38(1), e12485. https://doi.org/10.1111/exsy.12485
Published
Issue
Section
License
Copyright (c) 2022 Journal of Information and Communication Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Research impact
Harvested 2026-09-22Counts differ between services because each indexes a different body of literature. None of them is the whole picture.
2002 - 2020
