MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641107044 A) filed by Kkr & Ksr Institute Of Technology And Sciences on September 05, 2026, for A Blockchain-Integrated Privacy-Preserving Machine Learning Model For Chronic Kidney Disease Risk Assessment.

Inventors include Mr. Prof. Repudi Ramesh; Mrs. Md. Parveen Begum; M. Sai Sanjana; M. Srilakshmi; M. Lakshmitriveni; and M. Annamani.

The application for the patent was published on September 11, 2026, under issue no. 37/2026.

Abstract: ABSTRACT [0011] Chronic kidney disease affects over eight hundred million people worldwide and is frequently diagnosed at advanced stages, leading to increased health risks and mortality. The growing prevalence of chronic kidney disease, primarily driven by diabetes and hypertension, places a substantial burden on global healthcare systems. Artificial intelligence has demonstrated significant potential in enabling early and accurate prediction of chronic kidney disease; however, conventional artificial intelligence models suffer from challenges related to data centralization, privacy concerns, and limited transparency. To address these issues, this invention proposes a decentralized chronic kidney disease prediction framework that integrates blockchain technology with explainable artificial intelligence. The proposed system enables secure and privacy-preserving collaborative learning among healthcare institutions. Blockchain, smart contracts, and the InterPlanetary File System facilitate secure, transparent, and distributed model sharing. Furthermore, the incorporation of local interpretable model-agnostic explanations enhances model interpretability and clinical trust, supporting ethical and reliable artificial intelligence-driven decision-making in healthcare environments. The system analyzes patient health parameters such as blood pressure, sugar levels, and creatinine using machine learning models while ensuring that sensitive patient data remains local to each institution. This approach provides a privacy-preserving, reliable, and ethical solution for early chronic kidney disease detection that supports better clinical decision- making and improved healthcare outcomes.

Disclaimer: Curated by HT Syndication.