Predicting the onset of diabetes-related complications after a diabetes diagnosis with machine learning algorithms
Author
Mora, Toni
Roche, David
Rodríguez-Sánchez, Beatriz
Publication date
2023ISSN
0168-8227
Abstract
Aims: Using machine learning algorithms and administrative data, we aimed to predict the risk of being diagnosed with several diabetes-related complications after one-, two- and three-year post-diabetes diagnosis. Methods: We used longitudinal data from administrative registers of 610,019 individuals in Catalonia with a diagnosis of diabetes and checked the presence of several complications after diabetes onset from 2013 to 2017: hypertension, renal failure, myocardial infarction, cardiovascular disease, retinopathy, congestive heart failure, cerebrovascular disease, peripheral vascular disease and stroke. Four different machine learning (ML) algorithms (logistic regression (LR), Decision tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB)) will be used to assess their prediction performance and to evaluate the prediction accuracy of complications changes over the period considered. Results: 610,019 people with diabetes were included. After three years since diabetes diagnosis, the area under the curve values ranged from 60% (retinopathy) to 69% (congestive heart failure), whereas accuracy rates varied between 60% (retinopathy) to 75% (hypertension). RF was the most relevant technique for hypertension, myocardial and retinopathy, and LR for the rest of the comorbidities. The Shapley additive explanations values showed that age was associated with an elevated risk for all diabetes-related complications except retinopathy. Gender, other comorbidities, co-payment levels and age were the most relevant factors for comorbidity diagnosis prediction. Conclusions: Our ML models allow for the identification of individuals newly diagnosed with diabetes who are at increased risk of developing diabetes-related complications. The prediction performance varied across complications but within acceptable ranges as prediction tools.
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
61 - Medical sciences
Keywords
Diabetis mellitus
Complicacions relacionades amb la diabetis
Aprenentatge automàtic
Aprenentatge profund
Dades administratives
Diabetes mellitus
Complicaciones relacionadas con la diabetes
Aprendizaje automático
Aprendizaje profundo
Datos administrativos
Diabetes mellitus
Diabetes-related complications
Machine learning
Deep learning
Administrative data
Pages
7
Publisher
Elsevier
Collection
204
Is part of
Diabetes Research and Clinical Practice
Citation
Mora, Toni; Roche, David; Rodríguez-Sánchez, Beatriz [et al.]. Predicting the onset of diabetes-related complications after a diabetes diagnosis with machine learning algorithms. Diabetes Research and Clinical Practice, 2023, 204, 110910. Disponible en: <https://www.sciencedirect.com/science/article/pii/S0168822723006733?via%3Dihub>. Fecha de acceso: 17 oct. 2023. DOI: 10.1016/j.diabres.2023.110910
This item appears in the following Collection(s)
- Ciències de la Salut [738]
Rights
Under a Creative Commons license.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc/4.0/