Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.

ABSTRACT:

Background

Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed.

Objective

We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction.

Methods

We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes eti

SUBMITTER: Mistry S 

PROVIDER: S-EPMC9834645 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature