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

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 centralized data storage and processing, thus avoiding privacy issues. This marks the first application of federated learning to predict diabetes using real clinical datasets in Canada extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) without crossprovince patient data sharing. We address class-imbalance issues through downsampling techniques and compare federated learning performance against province-based and centralized models. Experimental results show that the federated MLP model presents a similar or higher performance compared to the model trained with the centralized approach. However, the federated logistic regression model showed inferior performance compared to its centralized peer. Introduction Predicting diabetes based on patient risk factors is paramount for the Canadian and global populations due to its significant impact on public health and healthcare costs. The number of patients with chronic disease, including diabetes, in Ontario, Canada alone, increased by 11.0% over the 10-year study period to 9.8 million in 2017/18, and the number with multimorbidity increased by 12.2% to 6.5 million