Kidney failure is one of the global health issues with a continuously increasing prevalence. Early identification of this disease is very important to avoid more severe kidney damage. Various diagnostic methods have been developed to detect kidney failure, both through laboratory techniques, medical imaging, and new sensor-based and artificial intelligence technologies. This article discusses the comparison of several commonly used methods in the identification of kidney failure. In this article, the comparison aims to provide an overview of the effectiveness of various methods in diagnosing kidney disease and how the combination of several approaches can enhance the quality of detection and management of kidney disease. The results of this comparison are expected to provide useful insights for the development of more effective diagnostic methods in detecting early-stage kidney failure, which in turn can reduce the global burden of kidney disease.
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