Cited by 407Artificial Intelligence in HealthcareMachine Learning in HealthcareDiabetes Management and Research

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Machine Learning Methods to Predict Diabetes Complications

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Dagliati, Arianna, Marini, Simone, Sacchi, Lucia, Cogni, Giulia, Teliti, Marsida, Tibollo, Valentina, De Cata, Pasquale, Chiovato, Luca, Bellazzi, Riccardo·2018·Journal of Diabetes Science and Technology

DOI 10.1177/1932296817706375PMC PMC5851210

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Abstract

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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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: Dagliati, Arianna
Marini, Simone
Sacchi, Lucia
Cogni, Giulia
Teliti, Marsida
Tibollo, Valentina
Chiovato, Luca
Bellazzi, Riccardo
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)
DOI: 10.1177/1932296817706375
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
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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

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