Hybrid Binary Grey Wolf Optimizer and Binary Sine Cosine Algorithms for Feature Selection Parkinson Disease
Keywords:
Parkinson, HBGWO-BSCA, HGWO-SCA, Feature SelectionAbstract
The algorithm used in the feature selection process is Hybrid Binary Grey Wolf Optimizer-Binary Sine Cosine Algorithm (HBGWO-BSCA). The dataset used for the HBGWO-BSCA is Parkinson's. This dataset is used because Parkinson's disease is one of the most frequently discussed diseases throughout the world. The aim of this paper is to find out what features are used in the process of predicting Parkinson's disease. The HBGWO-BSC feature selection algorithm was proven to be able to increase the accuracy value of the KNN classification algorithm by 92%, while the HGWO-SCA only obtains an accuracy of 88%. The value of the HBGWO-BSCA is higher than that of HGWO-SCA because the relationship between features selected with the HBGWO BSCA is more accurate than that of the Hybrid Grey Wolf Optimizer-Sine Cosine Algorithm (HGWO-SCA). This proves that the HBGWO-BSC feature selection algorithm obtains the highest accuracy, precision, recall and F1-Score values compared to the HGWO-SCA. The HBGWO-BSCA in feature selection uses parameters N and alpha with parameter value ranges N= 9-11 and alpha=2-8. HBGWO-BSCA is an algorithm used for the feature selection process in datasets that already have labels or classes.
References
H. K. Bharadwaj et al., “A review on the role of machine learning in enabling IoT based healthcare applications,” IEEE Access, vol. 9, pp. 38859–38890, 2021. https://doi.org/10.1109/ACCESS.2021.3059858.
M. Velu et al., “Human pathogenic monkeypox disease recognition using Q-Learning approach,” Diagnostics, vol. 13, no. 8, 1491, 2023. https://doi.org/10.3390/diagnostics13081491.
E. J. Formeister, R. T. Baum, and J. D. Sharon, “Supervised machine learning models for classifying common causes of dizziness,” Am. J. Otolaryngol. - Head Neck Med. Surg., vol. 43, no. 3, p. 103402, 2022. https://doi.org/10.1016/j.amjoto.2022.103402.
E. Celik and S. I. Omurca, “Improving Parkinson’s disease diagnosis with machine learning methods,” Proceedings of the 2019 Sci. Meet. Electr. Biomed. Eng. Comput. Sci. EBBT’2019, 2019, pp. 1–4. https://doi.org/10.1109/EBBT.2019.8742057.
D. Gupta et al., “Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease,” Cogn. Syst. Res., vol. 52, pp. 36–48, 2018. https://doi.org/10.1016/j.cogsys.2018.06.006.
M. R. Salmanpour, M. Shamsaei, and A. Rahmim, “Feature selection and machine learning methods for optimal identification and prediction of subtypes in Parkinson’s disease,” Comput. Methods Programs Biomed., vol. 206, p. 106131, 2021. https://doi.org/10.1016/j.cmpb.2021.106131.
R. Ramasamy Rajammal, S. Mirjalili, G. Ekambaram, and N. Palanisamy, “Binary Grey Wolf Optimizer with mutation and adaptive k-nearest neighbour for feature selection in Parkinson’s disease diagnosis,” Knowledge-Based Syst., vol. 246, p. 108701, 2022. https://doi.org/10.1016/j.knosys.2022.108701.
W. Wang, J. Lee, F. Harrou, and Y. Sun, “Early detection of Parkinson’s disease using deep learning and machine learning,” IEEE Access, vol. 8, pp. 147635–147646, 2020. https://doi.org/10.1109/ACCESS.2020.3016062.
M. Sivakumar, A. H. Christinal, and S. Jebasingh, “Parkinson’s disease diagnosis using a combined deep learning approach,” Proceedings of the 2019 2021 3rd Int. Conf. Signal Process. Commun. ICPSC 2021, no. May, pp. 81–84, 2021. https://doi.org/10.1109/ICSPC51351.2021.9451719.
N. Mahendran and D. R. V. P M, “A deep learning framework with an embedded-based feature selection approach for the early detection of the Alzheimer’s disease,” Comput. Biol. Med., vol. 141, p. 105056, 2022. https://doi.org/10.1016/j.compbiomed.2021.105056.
J. Hu et al., “Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection,” Knowledge-Based Syst., vol. 237, p. 107761, 2022. https://doi.org/10.1016/j.knosys.2021.107761.
M. S. Abbasi, H. Al-Sahaf, M. Mansoori, and I. Welch, “Behavior-based ransomware classification: A particle swarm optimization wrapper-based approach for feature selection,” Appl. Soft Comput., vol. 121, p. 108744, 2022. https://doi.org/10.1016/j.asoc.2022.108744.
Y. Guo, Z. Zhang, and F. Tang, “Feature selection with kernelized multi-class support vector machine,” Pattern Recognit., vol. 117, p. 107988, 2021. https://doi.org/10.1016/j.patcog.2021.107988.
Q. M. Alzubi, M. Anbar, Y. Sanjalawe, M. A. Al-Betar, and R. Abdullah, “Intrusion detection system based on hybridizing a modified binary grey wolf optimization and particle swarm optimization,” Expert Syst. Appl., vol. 204, p. 117597, 2022. https://doi.org/10.1016/j.eswa.2022.117597.
A. M. Ibrahim, M. A. Tawhid, and R. K. Ward, “A binary water wave optimization for feature selection,” Int. J. Approx. Reason., vol. 120, pp. 74–91, 2020. https://doi.org/10.1016/j.ijar.2020.01.012.
R. A. Ibrahim, M. Abd Elaziz, A. A. Ewees, M. El-Abd, and S. Lu, “New feature selection paradigm based on hyper-heuristic technique,” Appl. Math. Model., vol. 98, pp. 14–37, 2021. https://doi.org/10.1016/j.apm.2021.04.018.
O. M. Alyasiri, Y. N. Cheah, A. K. Abasi, and O. M. Al-Janabi, “Wrapper and hybrid feature selection methods using metaheuristic algorithms for english text classification: A systematic review,” IEEE Access, vol. 10, pp. 39833–39852, 2022. https://doi.org/10.1109/ACCESS.2022.3165814.
S. Bashir, I. U. Khattak, A. Khan, F. H. Khan, A. Gani, and M. Shiraz, “A novel feature selection method for classification of medical data using filters, wrappers, and embedded approaches,” Complexity, vol. 2022, 8190814, pp. 1-12, 2022. https://doi.org/10.1155/2022/8190814.
S. R. Kamel, R. YaghoubZadeh, and M. Kheirabadi, “Improving the performance of support-vector machine by selecting the best features by Grey Wolf algorithm to increase the accuracy of diagnosis of breast cancer,” J. Big Data, vol. 6, no. 1, 90, 2019. https://doi.org/10.1186/s40537-019-0247-7.
M. Abdel-Basset, K. M. Sallam, R. Mohamed, I. Elgendi, K. Munasinghe, and O. M. Elkomy, “An Improved binary Grey-Wolf Optimizer with simulated annealing for feature selection,” IEEE Access, vol. 9, pp. 139792–139822, 2021. https://doi.org/10.1109/ACCESS.2021.3117853.
Q. Al-Tashi, S. J. Abdul Kadir, H. M. Rais, S. Mirjalili, and H. Alhussian, “Binary optimization using hybrid Grey Wolf Optimization for feature selection,” IEEE Access, vol. 7, pp. 39496–39508, 2019. https://doi.org/10.1109/ACCESS.2019.2906757.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014. https://doi.org/10.1016/j.advengsoft.2013.12.007.
M. Shariati et al., “A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement,” Eng. Comput., vol. 38, no. 1, pp. 757–779, 2022. https://doi.org/10.1007/s00366-020-01081-0.
J. Hu et al., “Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection,” Knowledge-Based Syst., vol. 213, p. 106684, 2021. https://doi.org/10.1016/j.knosys.2020.106684.
M. H. Hashem, H. S. Abdullah, and K. I. Ghathwan, “Grey Wolf Optimization algorithm: A surve,” Iraqi Journal of Science, vol. 64, no. 11, pp. 5964–5984, 2023. https://doi.org/10.24996/ijs.2023.64.11.40.
T. Thaher, M. Awad, M. Aldasht, A. Sheta, H. Turabieh, and H. Chantar, “An enhanced evolutionary based feature selection approach using Grey Wolf Optimizer for the classification of high-dimensional biological data,” JUCS - Journal of Universal Computer Science, vol. 28, issue 5, pp. 499-539, 2022. https://doi.org/10.3897/jucs.78218.
M. H. Suid, M. A. Ahmad, M. R. T. R. Ismail, M. R. Ghazali, A. Irawan, and M. Z. Tumari, “An improved sine cosine algorithm for solving optimization problems,” Proceedings of the 2019 2018 IEEE Conf. Syst. Process Control. ICSPC’2018, December 2018, pp. 209–213, 2018. https://doi.org/10.1109/SPC.2018.8703982.
B. H. Abed-alguni, N. A. Alawad, M. A. Al-Betar, and D. Paul, “Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection,” Appl. Intell., vol. 53, pp. 13224–13260, 2023. https://doi.org/10.1007/s10489-022-04201-z.
Y. Fan, S. Zhang, H. Yang, D. Xu, and Y. Wang, “An improved future search algorithm based on the sine cosine algorithm for function optimization problems,” IEEE Access, vol. 11, pp. 30171–30187, 2023. https://doi.org/10.1109/ACCESS.2023.3258970.
R. Hans and H. Kaur, “Hybrid binary sine cosine algorithm and ant lion optimization (SCALO) approaches for feature selection problem,” Int. J. Comput. Mater. Sci. Eng., vol. 9, no. 1, pp. 1–26, 2020. https://doi.org/10.1142/S2047684119500210.
H. Xian, C. Yang, H. Wang, and X. Yang, “A modified sine cosine algorithm with teacher supervision learning for global optimization,” IEEE Access, vol. 9, no. 2, pp. 17744–17766, 2021. https://doi.org/10.1109/ACCESS.2021.3054053.
S. Alam Ansari and A. Zafar, “A fusion of dolphin swarm optimization and improved sine cosine algorithm for automatic detection and classification of objects from surveillance videos,” Meas. J. Int. Meas. Confed., vol. 192, 110921, 2022. https://doi.org/10.1016/j.measurement.2022.110921.
Y. Wan, A. Ma, L. Zhang, and Y. Zhong, “Multiobjective sine cosine algorithm for remote sensing image spatial-spectral clustering,” IEEE Trans. Cybern., vol. 52, no. 10, pp. 11172–11186, 2022. https://doi.org/10.1109/TCYB.2021.3064552.
Y. Chun and X. Hua, “Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy,” IEEE Access, vol. 11, pp. 73053–73061, 2023. https://doi.org/10.1109/ACCESS.2023.3294993.
J. C. Bansal, P. Bajpai, A. Rawat, and A. K. Nagar, “Sine cosine algorithm for discrete optimization problems,” In: Sine Cosine Algorithm for Optimization. Springer Briefs in Applied Sciences and Technology(). Springer, Singapore, 2023, pp. 65–86. https://doi.org/10.1007/978-981-19-9722-8_4.
P. C. Chiu, A. Selamat, O. Krejcar, and K. K. Kuok, “Hybrid sine cosine and fitness dependent optimizer for global optimization,” IEEE Access, vol. 9, pp. 128601–128622, 2021. https://doi.org/10.1109/ACCESS.2021.3111033.
M. M. Eid, E. S. M. El-Kenawy, and A. Ibrahim, “A binary sine cosine-modified whale optimization algorithm for feature selection,” Proceedings of the 2019 2021 IEEE 4th Natl. Comput. Coll. Conf. NCCC 2021, 2021, pp. 1-6. https://doi.org/10.1109/NCCC49330.2021.9428794.
S. Yaman, B. Karakaya, and Y. Erol, “A novel normalization algorithm to facilitate pre-assessment of Covid-19 disease by improving accuracy of CNN and its FPGA implementation,” Evol. Syst., vol. 14, pp. 581–591, 2023. https://doi.org/10.1007/s12530-022-09419-3.
K. Zhao, Z. Xu, M. Yan, T. Zhang, D. Yang, and W. Li, “A comprehensive investigation of the impact of feature selection techniques on crashing fault residence prediction models,” Inf. Softw. Technol., vol. 139, p. 106652, 2021. https://doi.org/10.1016/j.infsof.2021.106652.
Z. Gao, L. Ding, Q. Xiong, Z. Gong, and C. Xiong, “Image compressive sensing reconstruction based on z-Score standardized group sparse representation,” IEEE Access, vol. 7, pp. 90640–90651, 2019. https://doi.org/10.1109/ACCESS.2019.2927009.
V. Aggarwal, V. Gupta, P. Singh, K. Sharma, and N. Sharma, “Detection of spatial outlier by using improved Z-score test,” Proceedings of the 2019 Int. Conf. Trends Electron. Informatics, ICOEI 2019, 2019, pp. 788–790. https://doi.org/10.1109/ICOEI.2019.8862582.
P. C. Sahu, R. C. Prusty, and S. Panda, “Approaching hybridized GWO-SCA based type-II fuzzy controller in AGC of diverse energy source multi area power system,” J. King Saud Univ. - Eng. Sci., vol. 32, no. 3, pp. 186–197, 2020. https://doi.org/10.1016/j.jksues.2019.01.004.
N. Singh and S. B. Singh, “A novel hybrid GWO-SCA approach for optimization problems,” Eng. Sci. Technol. an Int. J., vol. 20, no. 6, pp. 1586–1601, 2017. https://doi.org/10.1016/j.jestch.2017.11.001.
W. Fu, J. Tan, X. Zhang, T. Chen, and K. Wang, “Blind parameter identification of MAR model and mutation hybrid GWO-SCA optimized SVM for fault diagnosis of rotating machinery,” Complexity, vol. 2019, 3264969, pp. 1-17, 2019. https://doi.org/10.1155/2019/3264969.
R. Devarapalli, B. V. Rao, and A. Al-Durra, “Optimal parameter assessment of solar photovoltaic module equivalent circuit using a novel enhanced hybrid GWO-SCA algorithm,” Energy Reports, vol. 8, pp. 12282–12301, 2022. https://doi.org/10.1016/j.egyr.2022.09.069.
W. Fu, K. Wang, C. Li, and J. Tan, “Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM,” Energy Convers. Manag., vol. 187, pp. 356–377, 2019. https://doi.org/10.1016/j.enconman.2019.02.086.
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