The Use of Federated Learning in AI-Based Predictive Analytics to Prevent Chronic Diseases in Global Health Systems

Abstract

The growing burden of the chronic diseases in the world like diabetes, cardiovascular diseases and chronic respiratory illnesses puts greater strain in the global health systems. Early interventions can be achieved by timely treating persons at the risk stage to minimize morbidity, mortality, and healthcare expenses. Nonetheless, the development of predictive models to prevent chronic diseases at the global level is fraught with a number of issues, such as regulating the privacy of data, the unequal presence of different data related to them in institutions and geographical locations, and the inability to unify sensitive patient-related data. The article discusses the use of federated learning (FL) as a powerful privacy-friendly framework that allows AI-based predictive analytics to be enabled in distributed health systems and, thus, promote chronic disease prevention and worldwide public health efforts. With traditional central machine learning models, training data needs to be consolidated into one site, which is usually inconsistent with patient privacy laws like HIPAA and GDPR, and questions data security and control by the institution. The federated learning achieves this by allowing various health institutions (e.g., hospitals, clinics, research centers) to jointly learn a single model without having to transfer raw patient data out of their local secured settings. The model is trained on local data at each site participating and only gradients or weights are sent to a central server that combine them into a global model. It is done to ensure that sensitive health information is stored on-premises, retaining patient confidentiality and also enjoying the advantages of a diversified and rich data of various populations and geographical locations. In the current paper, the authors describe a federated learning architecture that could be used to predict risks of chronic diseases: local data processing, feature selection in accordance with global guidelines, secure aggregation algorithms, and differential privacy to protect against possible inference attacks. We also mention how various types of data, such as structured electronic health records (EHRs), data of the lifestyle surveys, data of wearable devices, and social-determinant indicators can be incorporated into a single predictive model. With simulated cross-institutional data, we show that federated models perform similarly (e.g. in terms of area under the ROC curve, precision-recall metrics) to centralized models allowing data privacy to be maintained. In addition, generalizability of federated approach is better as models are conditioned in institutions with different countries of origin and socioeconomic status, and less bias can be introduced by region specific data. Besides technical feasibility, we note the more general health consequences: federated predictive analytics can allow early identification of high-risk people, make decisions on resource allocation, and promote prevention of interventions both at the community and policy levels. As an illustration, international health agencies might use federated models to track the trends of chronic disease risks in the regions, detect emerging hot spots, and apply interventions to them, including lifestyle counselling, mass screening, or mobile health (mHealth) outreach activities. The decentralized system of federated learning also makes collaboration between institutions in the high-income and low-to-middle-income countries possible, which promotes fair access to high-level AI power and does not necessitate centralized infrastructure, as well as does not undermine data sovereignty. Summing up, federated learning provides a potentially beneficial direction in scalable privacy-sensitive and shared predictive analytics to prevent chronic diseases across all health systems globally. This paradigm can enable institutions around the world to realize the power of their data, despite preserving patient privacy, by addressing the technical, ethical, and operational issues and difficulties. This practice is capable of revolutionizing the work of chronic disease preventions to allow early interventions and better health results at the global level.