Multi-party Diabetes Mellitus risk prediction based on secure federated learning

Abstract

At present, there is a lack of data exchange between medical institutions at the municipal level in the prevention and control of chronic disease. In this paper, we study Diabetes Mellitus risk prediction based on data securely shared between hospital and medical examination center, where federated learning algorithms are used and security challenges are analyzed. Targeted at a general disease prediction problem, we design customized secure protocols for regression family and tree family algorithms to solve the problem of potential private data leakage. Prediction of diabetes risks is taken as a typical example, and XGBoost, LightGBM, Neural Network, Logistic Regression algorithms are used for joint data modeling between different organizations, with the support from the “Seceum” Federated Learning Platform we first introduced in this paper. Extensive results on two datasets, including computational costs, recall and precision metrics, significant tests, show that using federated learning models we can make better use of the patient data between different organizations and deliver a reliable and improved prediction of Diabetes Mellitus risks.