Chronic Kidney Disease Classification Using Entropy Based Butterfly Optimization and Improved Artificial Neural Network Algorithm: Preprocessing, Feature Selection, Clustering, and Disease Prediction

M. Lincy Jacquline; N. Sudha

doi:10.25163/angiotherapy.869606

Angiogenesis, Inflammation & Therapeutics | Online ISSN 2207-872X

REVIEWS (Open Access)

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Chronic Kidney Disease Classification Using Entropy Based Butterfly Optimization and Improved Artificial Neural Network Algorithm: Preprocessing, Feature Selection, Clustering, and Disease Prediction

M. Lincy Jacquline ^1*, N. Sudha ¹

+ Author Affiliations

Journal of Angiotherapy 8 (6) 1-12 https://doi.org/10.25163/angiotherapy.869606

Submitted: 09 April 2024 Revised: 03 June 2024 Published: 12 June 2024

Abstract

Background: Chronic diseases, which are the leading cause of death globally, account for most medical expenses. Early detection of chronic diseases is crucial for effective preventative medicine. However, the complex nature of these diseases makes accurate early diagnosis challenging. Artificial intelligence (AI) technology has shown promise in assisting clinicians by automating diagnostic processes through predictive models. Methods: This study proposes a classification framework for chronic kidney disease (CKD) using an Enhanced Butterfly Optimization and Improved Artificial Neural Network (EBO-IANN) algorithm. The framework includes preprocessing using the K-means clustering (KMC) algorithm, feature selection via the EBO algorithm, clustering with Weighted Fuzzy C-means (WFCM), and classification using IANN. Results: The proposed EBO-IANN algorithm was evaluated on the CKD dataset from the UCI repository. The framework demonstrated superior performance compared to existing methods, including Bayesian, Neural Networks (NN), Modified K-means Support Vector Machine (MKSVM), and Enhanced Adaptive Neuro-Fuzzy Inference System (EANFIS). The EBO-IANN achieved higher precision, recall, F-measure, and accuracy. Specifically, it showed a precision of 98.9% and 95.9% for the CKD and Hungarian datasets, respectively, with no misclassified features. Conclusion: The EBO-IANN algorithm effectively enhances CKD classification by optimizing feature selection and classification accuracy. This approach can significantly aid in the early detection and treatment of CKD, potentially improving patient outcomes and reducing healthcare costs.

Keywords: Chronic Kidney Disease (CKD), EBO-IANN Algorithm, Preprocessing, Feature Selection, Clustering, Disease Classification

References

Amato, F., López, A., Peña-Méndez, E. M., Vanhara, P., Hampl, A., & Havel, J. (2013). Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11(2), 47-58. https://doi.org/10.2478/v10136-012-0031-x.

Aqlan, F., Markle, R., & Shamsan, A. (2017). Data mining for chronic kidney disease prediction. In IIE Annual Conference Proceedings, pp. 1789-1794.

Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: A novel approach for global optimization. Soft Computing, 23, 715-734. https://doi.org/10.1007/s00500-018-3102-4.

Brisimi, T. S., Xu, T., Wang, T., Dai, W., Adams, W. G., & Paschalidis, I. C. (2018). Predicting chronic disease hospitalizations from electronic health records: An interpretable classification approach. Proceedings of the IEEE, 106(4), 690-707. https://doi.org/10.1109/JPROC.2017.2789319.

Dwivedi, A. K. (2018). Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Computing and Applications, 29, 1545-1554. https://doi.org/10.1007/s00521-016-2701-1.

Ekanayake, I. U., & Herath, D. (2020). Chronic kidney disease prediction using machine learning methods. In Moratuwa Engineering Research Conference (MERCon) (pp. 260-265). https://doi.org/10.1109/MERCon50084.2020.9185249.

Elhoseny, M., Shankar, K., & Uthayakumar, J. (2019). Intelligent diagnostic prediction and classification system for chronic kidney disease. Scientific Reports, 9(1), Article 13366. https://doi.org/10.1038/s41598-019-46074-2.

El-Sappagh, S. H., & El-Masri, S. (2014). A distributed clinical decision support system architecture. Journal of King Saud University-Computer and Information Sciences, 26(1), 69-78. https://doi.org/10.1016/j.jksuci.2013.03.005.

Ghosh, P., Shamrat, F. J. M., Shultana, S., Afrin, S., Anjum, A. A., & Khan, A. A. (2020). Optimization of prediction method of chronic kidney disease using machine learning algorithm. In 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) (pp. 1-6). https://doi.org/10.1109/iSAI-NLP51646.2020.9376787.

Ismail Bin, M., & Dauda, U. (2013). Standardization and its effects on K-means clustering algorithm. Research Journal of Applied Sciences, Engineering and Technology, 6, 17.

Kriplani, H., Patel, B., & Roy, S. (2019). Prediction of chronic kidney diseases using deep artificial neural network technique. In M. A. Lebbah, A. Benyettou, & M. Sadok (Eds.), Computer Aided Intervention and Diagnostics in Clinical and Medical Images (pp. 179-187). Springer. https://doi.org/10.1007/978-3-030-04061-1_18.

Lambert, J. R., Arulanthu, P., & Perumal, E. (2020). Identification of nominal attributes for intelligent classification of chronic kidney disease using optimization algorithm. In International Conference on Communication and Signal Processing (ICCSP) (pp. 0119-0125). https://doi.org/10.1109/ICCSP48568.2020.9182206.

Nahato, K. B., Harichandran, K. N., & Arputharaj, K. (2015). Knowledge mining from clinical datasets using rough sets and backpropagation neural network. Computational and Mathematical Methods in Medicine, 2015, Article 460189. http://dx.doi.org/10.1155/2015/460189.

Nayak, J., Naik, B., & Behera, H. (2015). Fuzzy C-means (FCM) clustering algorithm: A decade review from 2000 to 2014. In Proceedings of the International Conference on Computational Intelligence in Data Mining (CIDM) (Vol. 2, pp. 133-149). https://doi.org/10.1007/978-81-322-2208-8_14.

Provenzano, M., Andreucci, M., De Nicola, L., Garofalo, C., Battaglia, Y., Borrelli, S., Gagliardi, I., Faga, T., Michael, A., Mastroroberto, P., & Serraino, G. F. (2020). The role of prognostic and predictive biomarkers for assessing cardiovascular risk in chronic kidney disease patients. International Journal of Nephrology, 2020, Article 2314128. https://doi.org/10.1155/2020/2314128.

Qin, J., Fu, W., Gao, H., & Zheng, W. X. (2016). Distributed k-means algorithm and fuzzy c-means algorithm for sensor networks based on multiagent consensus theory. IEEE Transactions on Cybernetics, 47(3), 772-783. https://doi.org/10.1109/TCYB.2016.2526683.

Tubishat, M., Alswaitti, M., Mirjalili, S., Al-Garadi, M. A., & Rana, T. A. (2020). Dynamic butterfly optimization algorithm for feature selection. IEEE Access, 8, 194303-194314. https://doi.org/10.1109/ACCESS.2020.3033757.

Vashisth, S., Dhall, I., & Saraswat, S. (2020). Chronic kidney disease (CKD) diagnosis using multi-layer perceptron classifier. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 346-350). https://doi.org/10.1109/Confluence47617.2020.9058178.

Yildirim, P. (2017). Chronic kidney disease prediction on imbalanced data by multilayer perceptron: Chronic kidney disease prediction. In 41st IEEE Annual Computer Software and Applications Conference (COMPSAC), Vol. 2 (pp. 193-198). https://doi.org/10.1109/COMPSAC.2017.84.

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