Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification

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Marai Ali
Faisal Khan
Muhammad Nouman Atta
Abdullah Khan
Asfandyar Khan


The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches.

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Nikam, S. S. “A comparative study of classification techniques in data mining algorithms”. Oriental Journal of Computer Science and Technology, 8(1), pp. 13-19, 2015.

Jayasingh, S. K., Gountia, D., Samal, N., & Chinara, P. K. “A Novel Approach for Data Classification Using Neural Networks”. IETE Journal of Research, 69(9), pp. 6022-6028, 2023.

A. Suragala, P. Venkateswarlu, and M. China Raju, "A comparative study of performance metrics of data mining algorithms on medical data," in ICCCE 2020: Proceedings of the 3rd International Conference on Communications and Cyber Physical Engineering, 2021, pp. 1549-1556: Springer.

D. A. Pisner and D. M. Schnyer, "Support vector machine," in Machine learning: Elsevier, 2020, pp. 101-121.

G. Latif, G. Ben Brahim, D. A. Iskandar, A. Bashar, and J. J. D. Alghazo, "Glioma Tumors’ classification using deep-neural-network-based features with SVM classifier," Diagnostics, vol. 12, no. 4, p. 1018, 2022.

Jusman, Y., Indra, Z., Salambue, R., Kanafiah, S. N. A. M., & Nurkholid, M. A. F. “Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data”. In Journal of Physics: Conference Series, vol. 1471, No. 1, p. 012043, 2020. IOP Publishing.

Y. Yang, P. Wang, and X. J. P. Gao, "A novel radial basis function neural network with high generalization performance for nonlinear process modelling," Processes, vol. 10, no. 1, p. 140, 2022.

Elansari, T., Ouanan, M., & Bourray, H. “Mixed Radial Basis Function Neural Network Training Using Genetic Algorithm”. Neural Processing Letters, vol.55 no. 8, pp. 10569-10587, 2023.

W. Yao, X. Chen, Y. Zhao, M. J. I. t. o. n. n. van Tooren, and l. systems, "Concurrent subspace width optimization method for RBF neural network modeling," IEEE transactions on neural networks and learning systems, vol. 23, no. 2, pp. 247-259, 2011.

V. Sharma, S. Rai, A. J. I. J. o. A. r. i. c. s. Dev, and s. engineering, "A comprehensive study of artificial neural networks," International Journal of Advanced research in computer science and software engineering, vol. 2, no. 10, 2012.

K. A. Rashedi, M. T. Ismail, N. N. Hamadneh, S. A. Wadi, J. J. Jaber, and M. J. J. o. M. Tahir, "Application of radial basis function neural network coupling particle swarm optimization algorithm to classification of Saudi Arabia stock returns," Journal of Mathematics, vol. 2021, pp. 1-8, 2021.

M. Z. Muda, A. R. Solis, and G. J. E. S. Panoutsos, "An evolving feature weighting framework for radial basis function neural network models," Expert Systems, vol. 40, no. 5, p. e13201, 2023.

C. J. P. R. A. Shao, "Data classification by quantum radial-basis-function networks," Physical Review A, vol. 102, no. 4, p. 042418, 2020.

A. Adamu, M. Abdullahi, S. B. Junaidu, and I. H. J. M. L. w. A. Hassan, "An hybrid particle swarm optimization with crow search algorithm for feature selection," Machine Learning with Applications, vol. 6, p. 100108, 2021.

B. Samieiyan, P. MohammadiNasab, M. A. Mollaei, F. Hajizadeh, and M. J. E. S. w. A. Kangavari, "Novel optimized crow search algorithm for feature selection," Expert Systems with Applications, vol. 204, p. 117486, 2022.

T. Thaher, A. Sheta, M. Awad, and M. J. E. S. w. A. Aldasht, "Enhanced variants of crow search algorithm boosted with cooperative based island model for global optimization," Expert Systems with Applications, vol. 238, p. 121712, 2024.

M. Pratiwi, J. Harefa, and S. J. P. C. S. Nanda, "Mammograms classification using gray-level co-occurrence matrix and radial basis function neural network," Procedia Computer Science, vol. 59, pp. 83-91, 2015.

R. Bhuvana, S. Purushothaman, R. Rajeswari, R. J. I. J. o. E. Balaji, and Technology, "Development of combined back propagation algorithm and radial basis function for diagnosing depression patients," International Journal of Engineering & Technology, vol. 4, no. 1, pp. 244-249, 2015.

S. Kaymak, A. Helwan, and D. J. P. c. s. Uzun, "Breast cancer image classification using artificial neural networks," Procedia computer science, vol. 120, pp. 126-131, 2017.

A. H. Osman and A. A. J. I. A. Alzahrani, "New approach for automated epileptic disease diagnosis using an integrated self-organization map and radial basis function neural network algorithm," IEEE Access, vol. 7, pp. 4741-4747, 2018.

R. R. Kouser, T. Manikandan, V. V. J. J. o. c. Kumar, and t. nanoscience, "Heart disease prediction system using artificial neural network, radial basis function and case based reasoning," Journal of computational and theoretical nanoscience, vol. 15, no. 9-10, pp. 2810-2817, 2018.

S. Alzaeemi, M. A. Mansor, M. M. Kasihmuddin, S. Sathasivam, M. J. I. J. o. E. E. Mamat, and C. Science, "Radial basis function neural network for 2 satisfiability programming," Indonesian Journal of Electrical Engineering and Computer Science, vol. 18, no. 1, pp. 459-469, 2020.

A. Jenkins, V. Gupta, and M. J. a. p. a. Lenoir, "General regression neural networks, radial basis function neural networks, support vector machines, and feedforward neural networks," arXiv preprint arXiv, 2019.

A. O. Ibrahim et al., "Classification of mammogram images using radial basis function neural network," in Emerging Trends in Intelligent Computing and Informatics: Data Science, Intelligent Information Systems and Smart Computing, vol. 4, pp. 311-320, 2020, Springer.

N. Tilahun, S. Sathasivam, and O. H. J. R. J. A. S. Choon, "Prey-predator algorithm as a new optimization technique using in radial basis function neural networks," Res J Appl Sci, vol. 8, no. 7, pp. 383-387, 2013.

Y. Jusman, Z. Indra, R. Salambue, S. N. A. M. Kanafiah, and M. A. F. Nurkholid, "Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data," in Journal of Physics: Conference Series, 2020, vol. 1471, no. 1, p. 012043: IOP Publishing.

S. A. Alzaeemi and S. J. P. Sathasivam, "Artificial immune system in doing 2-satisfiability based reverse analysis method via a radial basis function neural network," Processes, vol. 8, no. 10, p. 1295, 2020.

A. H. Fath, F. Madanifar, and M. J. P. Abbasi, "Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems," Petroleum, vol. 6, no. 1, pp. 80-91, 2020.

H. Lin, H. Dai, Y. Mao, and L. J. S. C. Wang, "An optimized radial basis function neural network with modulation-window activation function," Soft Computing, pp. 1-18, 2023.

J. de Jesus Rubio, D. Garcia, H. Sossa, I. Garcia, A. Zacarias, and D. J. E. Mujica-Vargas, "Energy processes prediction by a convolutional radial basis function network," Energy, vol. 284, p. 128470, 2023.

A. J. C. Askarzadeh and structures, "A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm," Computers & structures, vol. 169, pp. 1-12, 2016.

D. Lee, J. Kim, S. Shon, and S. J. A. S. Lee, "An Advanced Crow Search Algorithm for Solving Global Optimization Problem," Applied Sciences, vol. 13, no. 11, p. 6628, 2023.