Machine Learning for Predicting Micro-and Macrovascular Complications in Individuals With Prediabetes or Diabetes - Retrospective Cohort Study

Aug 4, 2026

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Micro- and macrovascular complications impose substantial morbidity and mortality in diabetes and may already develop during prediabetes. This retrospective cohort study builds machine learning models to identify individuals with prediabetes or diabetes who will develop micro- or macrovascular complications within the next 5 years. Electronic health records from Israel (2003-2013) were used, with risk targets including three microvascular and three macrovascular complication types, evaluated with logistic regression and gradient-boosted decision trees plus Shapley-based explanations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Background

Objective

Methods

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Background

source: [https://doi.org/10.48350/179310 | downloaded:](https://doi.org/10.48350/179310 | downloaded:) 11.3.2023 JOURNAL OF MEDICAL INTERNET RESEARCH Schallmoser et al Original Paper Machine Learning for Predicting Micro-and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study | Simon Schallmoser1,2, MSc; Thomas Zueger3,4, MD; Mathias Kraus5, PhD; Maytal Saar-Tsechansky6, PhD; Christoph Stettler3, MD; Stefan Feuerriegel1,2, PhD | | --- | | 1Institute ofAI in Management, LMU Munich, Munich, Germany 2Munich Center for Machine Learning (MCML), Munich, Germany
3Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital Bern, University of Bern, Bern, Switzerland 4Department of Endocrinology and Metabolic Diseases, Kantonsspital Olten, Olten, Switzerland
5Institute of Information Systems, FAU Erlangen-Nuremberg, Nuremberg, Germany
6The McCombs School of Business, The University of Texas at Austin, Austin, TX, United States
Corresponding Author:
Simon Schallmoser, MSc Institute of AI in Management LMU Munich
Geschwister-Scholl-Platz 1 Munich, 80539 Germany
Phone: 49 89 2180 6790
Email: [schallmoser@lmu.de](schallmoser@lmu.de)
Abstract | Background: Micro-and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential. Objective: This study aimed to build machine learning (ML) models that predict the risk of developing a micro-or macrovascular complication in individuals with prediabetes or diabetes. Methods: In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which ofthese individuals developed a micro-or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD) . Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs) . To explain the predictions of the GBDTs, we calculated Shapley additive explanations values. Results: Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations v

FAQs

Which complications were predicted in this study?

The study predicted three microvascular complications (retinopathy, nephropathy, neuropathy) and three macrovascular complications (peripheral vascular disease, cerebrovascular disease, and cardiovascular disease).

How were the machine learning models evaluated?

Performance was assessed using areas under the receiver operating characteristic curve for logistic regression and gradient-boosted decision trees across complication types and patient groups. The models were also interpreted using Shapley additive explanations for the GBDTs.

What factors were associated with higher risk according to the model explanations?

Higher levels of blood glucose, glycated hemoglobin, and serum creatinine were risk factors for microvascular complications. Age and hypertension were associated with elevated risk for macrovascular complications.

Aug 4, 2026

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