Predicting the Optimal Treatment for Diseases Using Whale Optimization Algorithm
DOI:
https://doi.org/10.31185/wjcms.273Keywords:
Big data, Machine learning, deep learning, Optimization, PredictionAbstract
Recently, there has been a significant research focus on addressing disease prevalence, especially in dealing with the complexities of big data associated with disease data. The challenge is achieving high accuracy due to missing value problems in big data. This study aims to use AI techniques to develop a system that predicates the optimal solutions for disease, regardless of the type of disease, i.e. the system can be applied to any type of disease. The approach involves handling missing values and normalizing disease datasets. The Whale Optimization Algorithm (WOA) will be used to improve predictions for effective disease treatments. We obtained good results for predicting the appropriate treatment for the disease in the proposed research, compared to the results obtained when applying the PSO algorithm before development in state of earlier, where the results obtained in the proposed research had higher accuracy than the results in in state of earlier at high iterations starting from 200 iterations and also had a lower error rate.:
References
N. H. Lameire et al., “Harmonizing acute and chronic kidney disease definition and classification: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference,” Kidney International, vol. 100, no. 3, pp. 516–526, Sep. 2021, doi: https://doi.org/10.1016/j.kint.2021.06.028.
E. Anderson and J. L. Durstine, “Physical activity, exercise, and Chronic diseases: a Brief Review,” Sports Medicine and Health Science, vol. 1, no. 1, pp. 3–10, Sep. 2019, doi: https://doi.org/10.1016/j.smhs.2019.08.006.
S. Sagiroglu and D. Sinanc, “Big data: a Review,” 2013 International Conference on Collaboration Technologies and Systems (CTS), pp. 42–47, May 2013, doi: https://doi.org/10.1109/cts.2013.6567202.
Ngiam, K. Y., & Khor, W.,” Big data and machine learning algorithms for health-care delivery” The Lancet Oncology, 20(5), e262-e273. Article (CrossRef Link)
F. P. S. Surbakti, W. Wang, M. Indulska, and S. Sadiq, “Factors influencing effective use of big data: A research framework,” Information & Management, vol. 57, no. 1, p. 103146, Feb. 2019, doi: https://doi.org/10.1016/j.im.2019.02.001.
R. Iqbal, F. Doctor, B. More, S. Mahmud, and U. Yousuf, “Big data analytics: Computational intelligence techniques and application areas,” Technological Forecasting and Social Change, vol. 153, p. 119253, Apr. 2018, doi: https://doi.org/10.1016/j.techfore.2018.03.024.
G. Carleo et al., “Machine learning and the physical sciences,” Reviews of Modern Physics, vol. 91, no. 4, Dec. 2019, doi: https://doi.org/10.1103/revmodphys.91.045002.
P. Mehta et al., “A high-bias, low-variance introduction to Machine Learning for physicists,” Physics Reports, vol. 810, pp. 1–124, May 2019, doi: https://doi.org/10.1016/j.physrep.2019.03.001.
M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science, vol. 349, no. 6245, pp. 255–260, Jul. 2020, doi: https://doi.org/10.1126/science.aaa8415.
J. E. van Engelen and H. H. Hoos, “A Survey on semi-supervised Learning,” Machine Learning, vol. 109, Nov. 2019, doi: https://doi.org/10.1007/s10994-019-05855-6.
Y. Matsuo et al., “Deep learning, reinforcement learning, and world models,” Neural Networks, Apr. 2022, doi: https://doi.org/10.1016/j.neunet.2022.03.037.
S. Sun, Z. Cao, H. Zhu, and J. Zhao, “A Survey of Optimization Methods From a Machine Learning Perspective,” IEEE Transactions on Cybernetics, vol. 50, no. 8, pp. 3668–3681, Aug. 2020, doi: https://doi.org/10.1109/tcyb.2019.2950779.
J. R. R. A. Martins and A. Ning, Engineering Design Optimization. Cambridge University Press, 2021. Accessed: Jul. 31, 2024. [Online]. Available: https://books.google.iq/books?hl=en&lr=&id=dBVEEAAAQBAJ&oi=fnd&pg=PR13&dq=%5B13%5D%09Martins.
M. Prosperi et al., “Causal inference and counterfactual prediction in machine learning for actionable healthcare,” Nature Machine Intelligence, vol. 2, no. 7, pp. 369–375, Jul. 2020, doi: https://doi.org/10.1038/s42256-020-0197-y.
W. Ben Chaabene, M. Flah, and M. L. Nehdi, “Machine learning prediction of mechanical properties of concrete: Critical review,” Construction and Building Materials, vol. 260, p. 119889, Nov. 2020, doi: https://doi.org/10.1016/j.conbuildmat.2020.119889.
S. N. Abed, “Predicting the Optimal Treatment for Diseases Using the Genetic Method by Develop (PSO) Optimization Technique,” Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 16, no. 2, Jul. 2024, doi: https://doi.org/10.29304/jqcsm.2024.16.21545.
A. Mostafa, Aboul Ella Hassanien, M. Houseni, and H. A. Hefny, “Liver segmentation in MRI images based on whale optimization algorithm,” Multimedia Tools and Applications, vol. 76, no. 23, pp. 24931–24954, Apr. 2017, doi: https://doi.org/10.1007/s11042-017-4638-5.
S. Mahmood, N. Z. Bawany, and M. R. Tanweer, “A comprehensive survey of whale optimization algorithm: modifications and classification,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 29, no. 2, p. 899, Feb. 2023, doi: https://doi.org/10.11591/ijeecs.v29.i2.pp899-910.
M. H. Nadimi-Shahraki, H. Zamani, Zahra Asghari Varzaneh, and Seyedali Mirjalili, “A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations,” Archives of Computational Methods in Engineering, May 2023, doi: https://doi.org/10.1007/s11831-023-09928-7
P. L. Tyack, “Social Organization of Baleen Whales,” pp. 147–175, Jan. 2022, doi: https://doi.org/10.1007/978-3-030-98449-6_7
J.-O. Meynecke et al., “The Role of Environmental Drivers in Humpback Whale Distribution, Movement and Behavior: A Review,” Frontiers in Marine Science, vol. 8, Nov. 2021, doi: https://doi.org/10.3389/fmars.2021.720774.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Sudad Abed, Zuhal Adel Madlool
This work is licensed under a Creative Commons Attribution 4.0 International License.
