2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 2 , PP: 152-190, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mohamed Saber 1 * , Ebrahim A. Mattar 2 , Marwa M. Eid 3 , El-Sayed M. El-kenawy 4
Doi: https://doi.org/10.54216/JISIoT.170211
Chronic Kidney Disease (CKD) is a global health concern that necessitates accurate and timely detection to improve patient outcomes and reduce healthcare costs. This study focuses on enhancing CKD classification using machine learning techniques, leveraging 400 instances with 25 clinical features to predict binary outcomes of CKD or non-CKD. The main objective is to improve detection accuracy by applying feature selection and model optimization. Standard machine learning models, including Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN), were employed, with optimization achieved through binary optimization algorithms such as Greylag Goose Optimization (GGO), Particle Swarm Optimization (PSO), Bat Algorithm (BA), and Whale Optimization Algorithm (WAO), along with hyperparameter tuning using genetic algorithms and other metaheuristics. Results indicate significant improvements in classification performance after feature selection and optimization, with the GGO-optimized MLP model achieving an accuracy of 97.06%. The contributions of this paper include (i) benchmarking baseline models for CKD detection, (ii) a comprehensive analysis of feature selection strategies, (iii) optimization of machine learning models for CKD classification, and (iv) visualization of model performance to aid future research in healthcare machine learning applications.
Chronic Kidney Disease , Machine Learning , Feature Selection , Hyperparameter Optimization , Healthcare Analytics
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