Privacy-First ML for Chronic Kidney Disease Prediction: Exploring a Decentralized Approach Using Blockchain and IPFS
Vikram Puri, Ishaani Priyadarshini, Aman Kataria, Sita Rani, Hong Min
Abstract
Chronic Kidney Disease (CKD) pose a global health challenge due to their increasing incidence and delayed detection, compounded by the burden they place on healthcare systems worldwide. This surge is particularly pronounced in both industrialized and emerging nations, fueled by the rising prevalence of conditions such as diabetes, hypertension, and obesity. While Artificial Intelligence (AI) shows promise for early CKD detection, issues with data security, model transparency, and decision-making trust have created uncertainty among both patients and healthcare providers. To tackle these issues, a decentralized collaborative learning framework utilizing explainable AI to detect CKD is proposed. This approach combines the privacy-preserving nature of blockchain technology with the interpretability of explainable AI models. A real-time environment was set up to conduct these experiments, simulating a network of healthcare providers collaborating on CKD detection while maintaining patient privacy.