Machine Learning Models for Prediction of Diabetic Microvascular Complications

Abstract

Importance and aims: Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN).

Methods: A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics.

Results: Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance.

Conclusions and relevance: There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.

Keywords: diabetes mellitus; machine learning; microvascular complications; risk prediction.

Figure 1.

Trend in publications of machine learning models by microvascular complications from 2002 to 2023. Symbols indicate the number of publications per year for diabetic retinopathy, diabetic kidney disease, and diabetic neuropathy. For 2023, the number of publications corresponds to the first seven months of the year.

Figure 2.

Country of affiliation for study authors by microvascular complication. Results reflect the proportion of studies reporting on the outcomes of diabetic retinopathy, diabetic kidney disease, and diabetic neuropathy. The data utilized for generating this visualization were acquired from PubMed and PubMed Central. We employed a combination of manual curation and the application of text mining functions that were developed using R software version 4.1.2. To arrive at the ultimate proportions of ancestries, we calculated them individually for each distinct study and subsequently aggregated them.

Figure 3.

Heat map of predictor variables included in machine learning models by microvascular complication. Proportion of models using the category of predictor variable. Abbreviations: eGFR, estimated glomerular filtration rate; LFT, liver function test; CBC, complete blood count; CMP, comprehensive metabolic panel; HDL, high-density lipoprotein; apo_a1, apolipoprotein A1; DM, diabetes mellitus; apo-b, apolipoprotein B.

Figure 4.

Model performance by ML technique for microvascular complications. Black squares indicate mean c-statistic; lines indicate minimum and maximum c-statistic for each ML model. Abbreviations: ML, machine learning; RF, Random Forest; LogReg, logistic regression; SVM, Support Vector Machines; GBM, Gradient Boosting Machines; SA, survival analysis; DT, Decision Trees; K-NN, K-Nearest Neighbors; NB, Naïve Bayes; LinReg, Linear Regression.