Comparison and development of machine learning tools in the prediction of chronic kidney disease progression.

ABSTRACT:

Background

Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up.

Objective

To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches.

Methods

The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome

SUBMITTER: Xiao J 

PROVIDER: S-EPMC6458616 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature