Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

ABSTRACT:

Background

Advanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine.

Hypothesis/objectives

To derive a model to predict the risk of cats developing chronic kidney disease (CKD) using data from electronic health records (EHRs) collected during routine veterinary practice.

Animals

A total of 106 251 cats that attended Banfield Pet Hospitals between January 1, 1995, and December 31, 2017.

Methods

Longitudinal EHRs from Banfield Pet Hospitals were extracted and randomly split into 2 parts. The first 67% of the data were used to build a prediction model, which included feature selection and identification of the optimal neural network type and architecture. The rem

SUBMITTER: Bradley R 

PROVIDER: S-EPMC6872623 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature