International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/2956 2020 2020 Department of Information Technology, Saveetha Engineering College, Chennai, Tamilnadu, 602 105, India M. M. Department of Information Technology(IT), VR Siddhartha Engineering College(A), Siddhartha Academy of Higher Education (Deemed to be University), Vijayawada, India M. Suneetha Department of Prosthetic Dentistry, Sechenov First Moscow State Medical University, Moscow, 119991, Russia Maria Mikhailova Department of CSE, Aditya College of Engineering & Technology, Surampalem, India Sripada NSVSC Ramesh Department of CSE, GITAM University, Visakhapatnam, India Kollati Vijaya Kumar Chronic kidney disease (CKD) is a non-communicable disease that has made a significant contribution to admission, morbidity, and mortality rates of patients globally. CKD is a common kidney disease that happens when both kidneys fail, and the CKD patient suffers from these conditions for a long time. Machine learning (ML) is becoming more crucial in medical diagnoses as it allows detailed examination, thus reducing human error and optimizing prediction accuracy. Now, ML classifiers and algorithms are highly dependable techniques for the diagnoses of diverse diseases such as diabetes, heart disease, liver disease, and tumor disease predictions. A neutrosophic set (NS) is especially suitable in applications where information is vague, incomplete, or inconsistent, which provides an effective means for analyzing and modeling intricate mechanisms. A NS is a mathematical approach to handle indeterminacy, uncertainty, and imprecision. It expands IF sets, classical sets, and fuzzy sets by introducing three degrees: truth (T), indeterminacy (I), and false (F). This manuscript offers a Double-Valued Neutrosophic Set for Chronic Kidney Disease Detection and Classification (DVNS-CKDDC) technique. In the DVNS-CKDDC technique, three major processes are involved. At the primary phase, the DVNS-CKDDC technique performs a linear scaling normalization (LSN) model. Next, the DVNS-CKDDC technique makes use of the DVNS model for the identification of CKD. Finally, the beluga whale optimization (BWO) algorithm is employed for the parameter tuning of the DVNS method. To ensure the supremacy of the DVNS-CKDDC technique, a widespread simulation analysis is involved. The experimental values stated that the DVNS-CKDDC approach attains improved performance over other models   2025 2025 279 290 10.54216/IJNS.250125 https://www.americaspg.com/articleinfo/21/show/2956