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 bivariate random effects model. Results: There were 12 eligible studies. The pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were 0.81 (95% confidence interval [CI] 0.67–0.90), 0.82 [95% CI 0.74–0.88], 4.55 [95% CI 3.07–6.75] and 0.23 [95% CI 0.13–0.42], respectively. The area under the summarized receiver operating characteristic curve was 0.88 (95% CI 0.85–0.91). Conclusions: Current ML algorithms have sufficient ability to help clinicians determine whether individuals will develop type 2 diabetes mellitus in the future. However, persons should be cautious before changing their attitude toward future diabetes risk after learning the result of the diabetes prediction test using ML algorithms.