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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Enhanced Optimized Classification Model of Chronic Kidney Disease

Author 1: Shahinda Elkholy
Author 2: Amira Rezk
Author 3: Ahmed Abo El Fetoh Saleh

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2023.0140239

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 2, 2023.

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Abstract: Chronic kidney disease (CKD) is one of the leading causes of death across the globe, affecting about 10% of the world's adult population. Kidney disease affects the proper function of the kidneys. As the number of people with chronic kidney disease (CKD) rises, it is becoming increasingly important to have accurate methods for detecting CKD at an early stage. Developing a mechanism for detecting chronic kidney disease is the study's main contribution to knowledge. In this study, preventive interventions for CKD can be explored using machine learning techniques (ML). The Optimized deep belief network (DBN) based on Grasshopper's Optimization Algorithm (GOA) classifier with prior Density-based Feature Selection (DFS) algorithm for chronic kidney disease is described in this study, which is called "DFS-ODBN." Prior to the DBN classifier, whose parameters are optimized using GOA, the proposed method eliminates redundant or irrelevant dimensions using DFS. The proposed DFS-ODBN framework consists of three phases, preprocessing, feature selection, and classification phases. Using CKD datasets, the suggested approach is also tested, and the performance is evaluated using several assessment metrics. Optimized-DBN achieves its maximum performance in terms of sensitivity, accuracy, and specificity, the proposed DFS-ODBN demonstrated accuracy of 99.75 percent using fewer features comparing with other techniques.

Keywords: Machine learning (ML); feature selection (FS); chronic kidney disease (CKD); deep belief network (DBN); grasshopper's optimization algorithm (GOA)

Shahinda Elkholy, Amira Rezk and Ahmed Abo El Fetoh Saleh, “Enhanced Optimized Classification Model of Chronic Kidney Disease” International Journal of Advanced Computer Science and Applications(IJACSA), 14(2), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140239

@article{Elkholy2023,
title = {Enhanced Optimized Classification Model of Chronic Kidney Disease},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140239},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140239},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Shahinda Elkholy and Amira Rezk and Ahmed Abo El Fetoh Saleh}
}


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