Objective

We aimed to identify machine learning (ML) models for type 2 diabetes (T2DM) prediction in community settings and determine their predictive performance.

Method

Systematic review of ML predictive modelling studies in 13 databases since 2009 was conducted. Primary outcomes included metrics of discrimination, calibration, and classification. Secondary outcomes included important variables, level of validation, and intended use of models. Meta-analysis of c-indices, subgroup analyses, meta-regression, publication bias assessments and sensitivity analyses were conducted.

Results

Twenty-three studies (40 prediction models) were included. Studies with high-, moderate-, and low- risk of bias were 3, 14, and 6 respectively. All studies conducted internal validation whereas none conducted external validation of their models. Twenty studies provided classification metrics to varying extents whereas only 7 studies performed model calibration. Eighteen studies reported information on both the variables used for model development and the feature importance. Twelve studies highlighted potential applicability of their models for T2DM screening. Meta-analysis produced a good pooled c-index (0.812). Sources of heterogeneity were identified through subgroup analyses and meta-regression. Issues pertaining to methodological quality and reporting were observed.

Conclusions

We found evidence of good performance of ML models for T2DM prediction in the community. Improvements to methodology, reporting and validation are needed before they can be used at scale.

中文翻译：

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机器学习模型在社区环境中对2型糖尿病的预测和使用：系统的回顾和荟萃分析。

目的

我们旨在确定社区环境中2型糖尿病（T2DM）预测的机器学习（ML）模型，并确定其预测性能。

方法

自2009年以来在13个数据库中进行了ML预测建模研究的系统评价。主要结果包括判别，校准和分类指标。次要结果包括重要变量，验证级别和模型的预期用途。进行了c指标的荟萃分析，亚组分析，荟萃回归，出版物偏倚评估和敏感性分析。

结果

包括23个研究（40个预测模型）。高，中度和低度偏倚风险的研究分别为3、14和6。所有研究均进行内部验证，而没有研究对其模型进行外部验证。二十项研究提供了不同程度的分类指标，而只有七项研究进行了模型校准。十八项研究报告了有关用于模型开发的变量和特征重要性的信息。十二项研究强调了其模型在T2DM筛查中的潜在适用性。荟萃分析得出良好的合并c指数（0.812）。通过亚组分析和荟萃回归确定了异质性的来源。观察到与方法学质量和报告有关的问题。

结论

我们在社区中发现了ML模型在T2DM预测中表现良好的证据。在大规模使用之前，需要改进方法，报告和验证。