Machine Learning Methods to Predict Diabetes Complications
Open accessAbstract
One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.
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Dagliati et al. reported that RF, SVM, and LR models using risk factors to predict development of diabetes-related complications provided an accuracy up to 0.838.
“final models, tailored in accordance with the complications, provided an accuracy up to 0.838”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Dagliati et al.97
The Abstract (page not provided) reports complication-specific final models with accuracy up to 0.838 and lists the risk factors used. The Results (page not provided) clarify that the final retained models were LR models: RF and SVM were evaluated, but the paper does not attribute that final-model accuracy to all three methods. (LLM, conf=0.88)
Experimental — accuracy not yet measuredFor the 3-year forecasting models in Dagliati et al. (2018), the AUC was 0.489 for SVM, 0.5 for RF, 0.726 for LR, and 0.533 for NB.
“We compared the AUCs for the 3‐year forecasting models, and the AUCs were 0.489 for the SVM model, 0.5 for the RF model, 0.726 for the LR and 0.533 for the NB model.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Dagliati et al. (2018)
The Results section says the paper evaluated AUC by forecasting scenario and modeling strategy, including three-year models. However, the available passages do not substantiate any of the four quoted three-year AUC values; Table 4 lists different AUCs for the final three-year models. Searches for the quoted values and Table 3 did not retrieve the numerical comparison. (LLM, conf=0.78)
Experimental — accuracy not yet measuredIn Dagliati et al. (2018), logistic regression after random forest imputation identified risk factors for microvascular complications.
“LR after RF imputation identified risk factors for microvascular complications.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Dagliati et al. (2018)
In Dagliati et al. (2018), SD or CIs were not reported.
“SD or CIs were not reported.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Dagliati et al. (2018)
In Dagliati et al. (2018), accuracy was reported only for LR.
“Accuracy was reported only for LR.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Dagliati et al. (2018)
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“Dagliati et al. (2018) reported AUC values for three different models for three different forecast periods of 3, 5 and 7 years.”
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- Authors
- Dagliati, A., Marini, S., Sacchi, L., Cogni, G., Teliti, M., Tibollo, V., De Cata, P., Chiovato, L., & Bellazzi, R.
- Affiliations
- University of Pavia (IT)
- Istituti Clinici Scientifici Maugeri (IT)
- IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy
- Corresponding author
- Bellazzi, Riccardo
- ORCID
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2017-05-12
- Volume
- 12
- Issue
- 2
- Pages
- 295-302
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC5851210
- OpenAlex
- W2612292012
- Cited by
- 407
- References
- 24
- Citations by year
- 2026: 31 · 2025: 55 · 2024: 71 · 2023: 60 · 2022: 53 · 2021: 62 · 2020: 44 · 2019: 21 · 2018: 9 · 2017: 1
- FWCI
- 28.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Diabetes Science and Technology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Funders
- Seventh Framework Programme (Mosaic)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- type 2 diabetes, diabetes complications, diabetic retinopathy, diabetic neuropathy, diabetic nephropathy, electronic health records, predictive modeling, logistic regression, stepwise feature selection, class imbalance, missing data, random forest, HbA1c, body mass index, hypertension, smoking, clinical decision support
- MeSH terms
- Machine Learning, Algorithms, Diabetes Mellitus, Type 2, Humans, Diabetes Complications, Data Mining
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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