Investigation into a Medical Diagnostic System for Chronic Kidney Disease: Preparation and Execution

This abstract provides a concise overview of a study focused on the development and execution of a medical diagnostic system specifically designed for Chronic Kidney Disease (CKD). The primary objective of this study is to contribute to the advancement of early detection and intervention strategies for chronic kidney disease (CKD), a prevalent and potentially incapacitating condition. The inquiry process encompasses several sequential stages, including the initial planning phase and subsequent execution phase. During this stage, comprehensive literature studies are conducted to examine the various approaches used for diagnosing chronic kidney disease (CKD), as well as the risk factors associated with its development and the biomarkers used for its detection. This literature review aids in identifying the deficiencies within the diagnostic system and highlights the potential utility of advanced techniques such as machine learning and data analytics. The development and implementation of the diagnostic system occur during the execution phase. A comprehensive database is established by the consolidation of data from various sources, including patient records, laboratory findings, and diagnostic imaging. State-of-the-art algorithms and machine learning approaches are utilized to conduct comprehensive data analysis, pattern identification, and the construction of prediction models for the diagnosis of Chronic Kidney Disease (CKD). This article provides a comprehensive examination of various healthcare systems pertaining to the management of chronic kidney disease. Additionally, it presents a systematic review of recent research undertaken by multiple scientists during the past five years.