English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper reviews a wide selection of machine learning models built topredict both the presence of diabetes and the presence of undiagnosed diabetesusing eight years of National Health and Nutrition Examination Survey (NHANES)data. Models are tuned and compared via their Brier Scores. The most relevantvariables of the best performing models are then compared. A Support VectorMachine with a linear kernel performed best for predicting diabetes, returninga Brier score of 0.0654 and an AUROC of 0.9235 on the test set. An elastic netregression performed best for predicting undiagnosed diabetes with a Brierscore of 0.0294 and an AUROC of 0.9439 on the test set. Similar features appearprominently in the models for both sets of models. Blood osmolality, familyhistory, the prevalance of various compounds, and hypertension are keyindicators for all diabetes risk. For undiagnosed diabetes in particular, thereare ethnicity or genetic components which arise as strong correlates as well.

Using Machine Learning Techniques to Identify Key Risk Factors for Diabetes and Undiagnosed Diabetes