Detection of factors affecting kidney function using machine learning methods.

ABSTRACT: Due to the increasing prevalence of chronic kidney disease and its high mortality rate, study of risk factors affecting the progression of the disease is of great importance. Here in this work, we aim to develop a framework for using machine learning methods to identify factors affecting kidney function. To this end classification methods are trained to predict the serum creatinine level based on numerical values of other blood test parameters in one of the three classes representing different ranges of the variable values. Models are trained using the data from blood test results of healthy and patient subjects including 46 different blood test parameters. The best developed models are random forest and LightGBM. Interpretation of the resulting model reveals a direct relationship between

SUBMITTER: Haratian A 

PROVIDER: S-EPMC9758148 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature