The Application of Machine Learning Algorithms to Diagnose CKD Stages and Identify Critical Metabolites Features

Background: Chronic kidney disease (CKD) is a progressive and heterogeneous disorder that affects kidney structures and functions. Now it becomes one of the major challenges of public health. Early-stage detection, specialized stage treatments can significantly defer or prevent the progress of CKDs. Currently, clinical CKD stage diagnoses are mainly based on the level of glomerular filtration rate (GFR). However, there are many different equations and approaches to estimate GFR, which can cause inaccurate and contradictory results.

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