Greylag Goose Optimization for Feature Selection and Hyperparameter Tuning in Chronic Kidney Disease Detection Mohamed Saber1,∗ , Ebrahim A. Mattar2 , Marwa M. Eid3,4 , El-Sayed M. El-kenawy5,6 1Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa City 11152, Egypt 2College of Engineering, University of Bahrain, Bahrain 3Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt 4 Jadara Research Center, Jadara University, Irbid 21110, Jordan 5Delta Higher Institute of Engineering and Technology, Department for Communications and Electronics, Mansoura 35511, Egypt 6Applied Science Research Center. Applied Science Private University, Amman, Jordan Emails: Mohamed.saber@deltauniv.edu.eg; ebmattar@uob.edu.bh; mmm@ieee.org; skenawy@ieee.org Abstract 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. Received: January 09, 2025 Revised: February 10, 2025 Accepted: March 04, 2025 Keywords: Chronic Kidney Disease; Machine Learning; Feature Selection; Hyperparameter Optimization; Healthcare Analytics