Urinary Bladder Inflammation Prediction with the Gray Wolf Optimization Algorithm and Multi-Layer Perceptron-Based Hybrid Architecture

Year 2023, , 1185 - 1194, 28.12.2023

Mehmet Akif Bülbül

https://doi.org/10.17798/bitlisfen.1360049

Cited By: 1

Abstract

In this study, a decision support system for bladder inflammation prediction is presented. The proposed decision support system is built by establishing a hybrid architecture with Gray wolf optimization algorithm (GWO) and Multi-layer perceptron (MLP) networks. In addition to optimizing the hyperparameters in the MLP structure with GWO, the hybrid architecture also optimizes the order of input values to be presented to the MLP structure. The Acute Inflammations data set in the UCI Machine Learning repository was used as the data set in the study. Classification operations were carried out on this data set with the models obtained with hybrid architecture, Decision trees, k-Nearest Neighbors and Support Vector Machines methods. The controversial findings presented as a result of experimental studies have shown that the proposed hybrid architecture produces more successful results than other machine learning methods used in the study. In addition, the MLP network structure optimized with the hybrid architecture offers a new diagnostic method in terms of patient decision support systems.

Keywords

Gray Wolf Optimization Algorithm, Multi-Layer Perceptron, Decision Support Systems.

References

• [1] G. Salanturoğlu, “The Effect of Agmatine on Experimentally Generated Acute Inflammation Models,” M.S. thesis, Health Sciences Institute, Marmara University, İstanbul, Turkey, 2005.
• [2] L. Heuft, J. Voigt, L. Selig, M. Stumvoll, H. Schlögl, and T. Kaiser, “Refeeding syndrome—diagnostic challenges and the potential of clinical decision support systems,” Dtsch. Arztebl. Int., 2023, doi: 10.3238/arztebl.m2022.0381.
• [3] M. A. Bülbül, “Performance of different membership functions in stress classification with fuzzy logic,” Bitlis Eren Univ. J. Sci. Technol., vol. 12, no. 2, pp. 60–63, 2022, doi: 10.17678/beuscitech.1190436.
• [4] K. Adem, S. Kiliçarslan, and O. Cömert, “Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification,” Expert Syst. Appl., vol. 115, pp. 557–564, 2019, doi: 10.1016/j.eswa.2018.08.050.
• [5] A. Giorgio, C. Guaragnella, and M. Rizzi, “FPGA-Based Decision Support System for ECG Analysis,” J. Low Power Electron. Appl., vol. 13, no. 1, p. 6, 2023, doi: 10.3390/jlpea13010006.
• [6] E. S. Kim, D. J. Shin, S. T. Cho, and K. J. Chung, “Artificial Intelligence-Based Speech Analysis System for Medical Support,” Int. Neurourol. J., vol. 27, no. 2, pp. 99-105, 2023, doi: 10.5213/inj.2346136.068.
• [7] M. Casal-Guisande, L. Ceide-Sandoval, M. Mosteiro-Añón, M. Torres-Durán, J. Cerqueiro-Pequeño, J. Bouza-Rodríguez, A. Fernández-Villar and A, Comesaña-Campos, “Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea,” Diagnostics, vol. 13, no. 11, p.1854, 2023, doi: 10.3390/diagnostics13111854.
• [8] A. Javeed, M. A. Saleem, A. L. Dallora, L. Ali, J. S. Berglund, and P. Anderberg, “Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning,” Appl. Sci., vol. 13, no. 8, p. 5188, 2023, doi: 10.3390/app13085188.
• [9] M. A. Bülbül, C. Öztürk, and M. F. Işık, “Optimization of Climatic Conditions Affecting Determination of the Amount of Water Needed by Plants in Relation to Their Life Cycle with Particle Swarm Optimization, and Determining the Optimum Irrigation Schedule,” Comput. J., 2021, doi: 10.1093/comjnl/bxab097.
• [10] M. F. Işık, F. Avcil, E. Harirchian, M. A. Bülbül, M. Hadzima-Nyarko, E. Işık, R. İzol, D. Radu, “A Hybrid Artificial Neural Network-Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings,” Sustainability, vol. 15, no. 12, p.1975, 2023, doi: 10.3390/su15129715.
• [11] F. Jeyafzam, B. Vaziri, M. Y. Suraki, A. A. R. Hosseinabadi, and A. Slowik, “Improvement of grey wolf optimizer with adaptive middle filter to adjust support vector machine parameters to predict diabetes complications,” Neural Comput. Appl., vol. 33, no. 22, pp. 15205-15228, 2021, doi: 10.1007/s00521-021-06143-y.
• [12] A. Magdy, H. Hussein, R. F. Abdel-Kader, and K. A. El Salam, “Performance Enhancement of Skin Cancer Classification using Computer Vision,” IEEE Access, vol. 11, pp.72120-72133, 2023, doi: 10.1109/ACCESS.2023.3294974.
• [13] E. Işık, N. Ademović, E. Harirchian, F. Avcil, A. Büyüksaraç, M. Hadzima-Nyarko, M. A. Bülbül, M. F. Işık and B. Antep, “Determination of Natural Fundamental Period of Minarets by Using Artificial Neural Network and Assess the Impact of Different Materials on Their Seismic Vulnerability,” Appl. Sci., vol. 13, no. 2, p. 809, 2023, doi: 10.3390/app13020809.
• [14] M. A. Bülbül and C. Öztürk, “Optimization, Modeling and Implementation of Plant Water Consumption Control Using Genetic Algorithm and Artificial Neural Network in a Hybrid Structure,” Arab. J. Sci. Eng., vol. 47, no. 2, pp. 2329-2343, 2022, doi: 10.1007/s13369-021-06168-4.
• [15] M. A. Bülbül, E. Harirchian, M. F. Işık, S. E. Aghakouchaki Hosseini, and E. Işık, “A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings,” Appl. Sci., vol. 12, no. 10, 2022, doi: 10.3390/app12105138.
• [16] M. Zhu, G. Zhang, L. Zhang, W. Han, Z. Shi, and X. Lv, “Object Segmentation by Spraying Robot Based on Multi-Layer Perceptron,” Energies, vol. 16, no. 1, p. 232, 2023, doi: 10.3390/en16010232.
• [17] H. Faris, I. Aljarah, M. A. Al-Betar, and S. Mirjalili, “Grey wolf optimizer: a review of recent variants and applications,” Neural Computing and Applications., vol. 30, no. 2, pp. 413-435, 2018. doi: 10.1007/s00521-017-3272-5.
• [18] D. S. Khafaga, E. S. M. El-kenawy, F.K. Karim, M. Abotaleb, A. Ibrahim, A. A. Abdelhamid, and D. L. Elsheweikh, “Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets,” Comput. Mater. Contin., vol. 74, no. 2, pp. 4531-4545, 2023, doi: 10.32604/cmc.2023.033042.
• [19] Y. Ou, P. Yin, and L. Mo, “An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning,” Biomimetics, vol. 8, no. 1, p.84, 2023, doi: 10.3390/biomimetics8010084.
• [20] T. C. Tai, C. C. Lee, and C. C. Kuo, “A Hybrid Grey Wolf Optimization Algorithm Using Robust Learning Mechanism for Large Scale Economic Load Dispatch with Vale-Point Effect,” Appl. Sci., vol. 13, no. 4. p.2727, 2023, doi: 10.3390/app13042727.
• [21] P. He and W. Wu, “Levy flight-improved grey wolf optimizer algorithm-based support vector regression model for dam deformation prediction,” Front. Earth Sci., vol. 11, 2023, doi: 10.3389/feart.2023.1122937.
• [22] A. I. Lawah, A. A. Ibrahim, S. Q. Salih, H. S. Alhadawi, and P. S. Josephng, “Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization,” IEEE Access, vol. 11, pp. 42416-42430, 2023, doi: 10.1109/ACCESS.2023.3266290.
• [23] K. Mehmood, N. I. Chaudhary, Z. A. Khan, K. M. Cheema, and M. A. Z. Raja, “Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model,” Biomimetics, vol. 8, no. 2, p.141, 2023, doi: 10.3390/biomimetics8020141.
• [24] N. Ji, R. Bao, X. Mu, Z. Chen, X. Yang, and S. Wang, “Cost-sensitive classification algorithm combining the Bayesian algorithm and quantum decision tree,” Front. Phys., vol. 11, 2023, doi: 10.3389/fphy.2023.1179868.
• [25] G. Vinayakumar, A. P. Alex, and V. S. Manju, “A Comparison of KNN Algorithm and MNL Model for Mode Choice Modelling,” Eur. Transp. - Trasp. Eur., no. 92, pp. 1-14, 2023, doi: 10.48295/ET.2023.92.3.
• [26] N. Vanitha, C. R. Rene Robin, and D. Doreen Hephzibah Miriam, “An Ontology Based Cyclone Tracks Classification Using SWRL Reasoning and SVM,” Comput. Syst. Sci. Eng., vol. 44, no. 3, pp. 2323-2336, 2023, doi: 10.32604/csse.2023.028309.
• [27] M. F. Akay, F. Abut, M. Özçiloğlu, and D. Heil, “Identifying the discriminative predictors of upper body power of cross-country skiers using support vector machines combined with feature selection,” Neural Comput. Appl., vol. 27, no. 6, pp. 1785-1796, 2016, doi: 10.1007/s00521-015-1986-9.
• [28] D. Chrimes, “Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19,” Interact. J. Med. Res., vol. 12, p.e42540, 2023, doi: 10.2196/42540.
• [29] C. Wang, J. Xu, J. Li, Y. Dong, and N. Naik, “Outsourced Privacy-Preserving kNN Classifier Model Based on Multi-Key Homomorphic Encryption,” Intell. Autom. Soft Comput., vol. 37, no.2, pp. 1421-1436, 2023, doi: 10.32604/iasc.2023.034123.
• [30] H. Nakao, M. Imaoka, M. Hida, R. Imai, M. Nakamura, K. Matsumoto, and K. Kita, “Determination of individual factors associated with hallux valgus using SVM-RFE,” BMC Musculoskelet. Disord., vol. 24, no. 1, 2023, doi: 10.1186/s12891-023-06303-2.
• [31] M. Lichman, “UCI Machine Learning Repositor,” Irvine, CA: University of California, School of Information and Computer Science, 2013.
• [32] H. Kahramanlı, “Determining the Acute Inflammations using Back Propagation Algorithm with Adaptive Learning Coefficients,” 2016. doi: 10.15242/dirpub.dir1216009.
• [33] M. A. Bülbül, “Optimization of artificial neural network structure and hyperparameters in hybrid model by genetic algorithm: iOS–android application for breast cancer diagnosis/prediction,” J. Supercomput., 2023, doi: 10.1007/s11227-023-05635-z.
• [34] C. Öztürk, M. Taşyürek, and M. U. Türkdamar, “Transfer learning and fine-tuned transfer learning methods’ effectiveness analyse in the CNN-based deep learning models,” Concurr. Comput. Pract. Exp., vol. 35, no. 4, 2023, doi: 10.1002/cpe.7542.
• [35] M. A. Bülbül, “A Hybrid Approach for Multiclass Classification of Dry Bean Seeds,” Journal of the Institute of Science and Technology., vol. 13, no. 1, pp. 33-43, 2023, doi: 10.21597/jist.1185949.
• [36] M. Taşyürek, “ODRP: a new approach for spatial street sign detection from EXIF using deep learning-based object detection, distance estimation, rotation and projection system,” Vis. Comput., 2023, doi: 10.1007/s00371-023-02827-9.