Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data

Presentation Time: 11:00 AM - 11:15 AM

Abstract Keywords: Data Mining, Disease Models, Machine Learning, Privacy and Security
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing healthcare service accuracy and accessibility. In particular, developing data-driven machine learning models can provide early identification of patients with high risk for diabetes, potentially leading to more effective therapeutic strategies and reduced healthcare costs. However, regulation restrictions create barriers to developing centralized predictive models. This paper addresses the challenges by introducing a federated learning approach, which amalgamates predictive models without central data storage, thus avoiding privacy issues. This marks the first application of federated learning to predict diabetes using real clinical datasets in Canada from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) that predicts diabetes diagnosis based on risk factors without cross-province patient data sharing. We address class-imbalance issues through downsampling techniques and compare federated learning performance against province-based and centralized models. This innovative approach demonstrates federated learning's potential to replace traditional centralized models, offering a privacy-conscious, effective solution for leveraging real-life clinical data in diabetes prediction.

Speaker(s):
Guojun Tang, PhD
University of Calgary

Author(s):
Steve Drew, PhD - University of Calgary; Tyler Williamson, PhD - University of Calgary; Jason Black, MSc - University of Calgary;