Predicting Progression of Type 2 Diabetes Using Primary Care Data with the Help of Machine Learning

Berk Ozturk, Tom Lawton, Stephen Smith, Ibrahim Habli

Research output: Chapter in Book/Report/Conference proceedingChapter


Type 2 diabetes is a life-long health condition, and as it progresses, A range of comorbidities can develop. The prevalence of diabetes has increased gradually, and it is expected that 642 million adults will be living with diabetes by 2040. Early and proper interventions for managing diabetes-related comorbidities are important. In this study, we propose a Machine Learning (ML) model for predicting the risk of developing hypertension for patients who already have Type 2 diabetes. We used the Connected Bradford dataset, consisting of 1.4 million patients, as our main dataset for data analysis and model building. As a result of data analysis, we found that hypertension is the most frequent observation among patients having Type 2 diabetes. Since hypertension is very important to predict clinically poor outcomes such as risk of heart, brain, kidney, and other diseases, it is crucial to make early and accurate predictions of the risk of having hypertension for Type 2 diabetic patients. We used Naïve Bayes (NB), Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM) to train our model. Then we ensembled these models to see the potential performance improvement. The ensemble method gave the best classification performance values of accuracy and kappa values of 0.9525 and 0.2183, respectively. We concluded that predicting the risk of developing hypertension for Type 2 diabetic patients using ML provides a promising stepping stone for preventing the Type 2 diabetes progression.

Original languageEnglish
Title of host publicationCaring is Sharing
Subtitle of host publicationExploiting the Value in Data for Health and Innovation
Number of pages5
ISBN (Electronic)978-1-64368-389-8
Publication statusPublished - 18 May 2023

Publication series

NameStudies in health technology and informatics
PublisherIOS Press
ISSN (Print)0926-9630

Bibliographical note

© 2023 European Federation for Medical Informatics (EFMI) and IOS Press.


  • Machine learning
  • AI
  • Diabetes Mellitus, Type 2/diagnosis
  • Healthcare
  • Machine Learning
  • Hypertension/diagnosis
  • Primary Health Care
  • Support Vector Machine

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