Federated Learning for Personalized Patient Stratification & Disease Prediction

the rapidly advancing field of healthcare, protecting patient privacy while enabling advanced analytics is a pressing challenge that demands innovative solutions. This project decisively employs Federated Learning (FL), a cutting-edge machine learning technique, to establish a secure and privacy-preserving system for predicting diabetes and stratifying patients based on crucial health indicators. Leveraging the Behavioral Risk Factor Surveillance System (BRFSS) 2015 dataset, which features binary indicators related to diabetes risk, the data is strategically divided among simulated clients categorized by age groups to effectively replicate a real-world federated environment. We utilize supervised learning methods, such as Random Forest and Logistic Regression, to accurately predict diabetes status. In parallel, unsupervised learning techniques, including K-Means clustering with PCA visualization, are deployed to segment patients into distinct risk-based clusters. This project systematically simulates FL by training local models on individual clients’ data and then averaging the model weights to form a robust global model, all while ensuring that no raw data is exchanged. The results affirm that FL can deliver precise disease predictions and actionable patient segmentation, all while rigorously maintaining data privacy. This initiative underscores the immense potential of FL in real-world healthcare systems, paving the way for personalized treatment strategies that fully respect confidentiality regulations.