Machine Learning Methods to Predict Diabetes Complications

Research output: Contribution to journalArticle

  • External authors:
  • Simone Marini
  • Lucia Sacchi
  • Giulia Cogni
  • Marsida Teliti
  • Valentina Tibollo
  • Pasquale De Cata
  • Luca Chiovato
  • Riccardo Bellazzi

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.

Bibliographical metadata

Original languageEnglish
Pages (from-to)295-302
Number of pages8
JournalJournal of Diabetes Science and Technology
Volume12
Issue number2
Early online date12 May 2017
DOIs
Publication statusPublished - 1 Mar 2018