Aims/Introduction

Recently, an increasing number of cohort studies have suggested using machine learning (ML) to predict type 2 diabetes mellitus (T2DM). However, its predictive ability remains inconclusive. This meta-analysis evaluated the current ability of ML algorithms for predicting incident T2DM.

Material and methods

We systematically searched longitudinal studies published from 1950 Jan. 1 to 2020 May 17 using MEDLINE and EMBASE. Included studies had to compare ML’s classification with the actual incidence of T2DM and present data on the number of true-positives, false-positives, true-negatives, and false-negatives. The dataset for these 4 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 T2DM 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.