Abstract
Nowadays, medical data classification plays an important role in healthcare informatics applications such as disease prediction, classification, etc. The recently developed machine learning and deep learning models are commonly employed for effective medical data classification, which can be applied for disease diagnosis. The existing techniques make few shortcomings in terms of computational complexity, higher-dimensional features, higher execution time, etc. To tackle these issues, this paper develops a new classification model with metaheuristic algorithm based optimal feature selection for Chronic Kidney Disease diagnosis. Initially, the data with missing values were evacuated in the pre-processing stage. Then, the best subset of features selected by a metaheuristic algorithm named Oppositional based FireFly Optimization algorithm, which helps in the prediction or classification of the disease more accurately. The incorporation of oppositional based learning concept helps to improve the convergence rate of FireFly algorithm. For classification, Deep Neural Network was proposed to diagnose the existence of CKD. The effectiveness of the proposed feature selection-based classifier was tested on the dataset as measures of sensitivity, specificity, and accuracy. The results concluded that the proposed algorithm achieved a high accuracy rate when compared to the algorithms of existing classifier models.
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We thank the UCI Machine Learning Repository for publishing attribute information of our own and original chronic kidney disease dataset: L.Jerlin Rubini, P.Eswaran & Dr.P.Soundarapandian, M.D., D.M (Senior Consultant Nephrologist). https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease
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Acknowledgements
We thank Dr.P.Soundarapandian, M.D., D.M (Senior Consultant Nephrologist) and Apollo Hospital, Karaikudi, India for their support and facility at the time of CKD data collection and preliminary study.
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This article has been written with the financial support of RUSA–Phase 2.0 grant sanctioned vide Letter No. F. 24-51/2014-U, Policy (TNMulti-Gen), Dept. of Edn. Govt. of India, Dt. 09.10.2018.
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Lambert, J.R., Perumal, E. Oppositional firefly optimization based optimal feature selection in chronic kidney disease classification using deep neural network. J Ambient Intell Human Comput 13, 1799–1810 (2022). https://doi.org/10.1007/s12652-021-03477-2
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DOI: https://doi.org/10.1007/s12652-021-03477-2