SynthesisJournal of diabetes investigation2022
Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta-analysis.
Synthesis in Journal of diabetes investigation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Unveiling the diagnostic power of lncRNAs in colorectal cancer: a meta-analysis.Biomedical engineering online · 2025Pooled it
- Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta-analysis.Journal of diabetes investigation · 2022Pooled it
- Review
- Race/Ethnicity and Other Predictors of Early-Onset Type 2 Diabetes Mellitus in the US Population.Journal of racial and ethnic health disparities · 2025Article
- Energy landscape analysis of health checkup data clarified multiple pathways to diabetes development in obese and non-obese subjects.Frontiers in endocrinology · 2025Observational
- A scoping review of artificial intelligence-based methods for diabetes risk prediction.NPJ digital medicine · 2023Article
- Data collaboration analysis in predicting diabetes from a small amount of health checkup data.Scientific reports · 2023Article
- From normal population to prediabetes and diabetes: study of influencing factors and prediction models.Frontiers in endocrinology · 2023Article
- Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type.International journal of environmental research and public health · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
AIMS/
introductionRecently, 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
methodsWe 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.
resultsThere 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).
conclusionsCurrent 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.