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

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Mar 1, 2020

Abstract

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

Authors

Kushan De Silva

Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund,Sweden.
Daniel Jönsson

Department of Periodontology, Malmö University, Malmö and Swedish Dental Service of Skane, Lund, Sweden.
Ryan T Demmer

Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.

Keywords

Adult Algorithms Area Under Curve Female Humans Machine Learning Male Middle Aged Models, Theoretical Nutrition Surveys Prediabetic State Prevalence Risk Factors ROC Curve Socioeconomic Factors

External Resources

View on PubMed Access via DOI PubMed (31889178)

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

Abstract

Authors

Keywords

External Resources

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