Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta-analysis.

ABSTRACT:

Aims/introduction

Recently, an increasing number of cohort studies have suggested using machine learning (ML) to predict type 2 diabetes mellitus. However, its predictive ability remains inconclusive. This meta-analysis evaluated the current ability of ML algorithms for predicting incident type 2 diabetes mellitus.

Materials and methods

We systematically searched longitudinal studies published from 1 January 1950 to 17 May 2020 using MEDLINE and EMBASE. Included studies had to compare ML's classification with the actual incidence of type 2 diabetes mellitus, and present data on the number of true positives, false positives, true negatives and false negatives. The dataset for these four values was pooled with a hierarchical summary receiver operating characteristic and a biva

SUBMITTER: Kodama S 

PROVIDER: S-EPMC9077721 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature