A combined strategy of feature selection and machine learning to identify predictors of prediabetes.

ABSTRACT:

Objective

To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population.

Materials and methods

We analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and ext

SUBMITTER: De Silva K 

PROVIDER: S-EPMC7647289 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature