MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641127022 A) filed by K. S. R. College Of Engineering on October 25, 2026, for Portable Urinary Sensor System For Early Detection Of Kidney Stone Risk Using Machine Learning.
Inventors include Mr. S Parameswaran; Ms. V. D. Nandhini; Dr. R Prabu; Ms. T S K Nishi; Ms. B Aathika Begam; Ms. T Sowmiya; Ms Fathima Safrin A; Ms Tharanya R; Mr. R. Kanishkar; Mr. P Ligin; and Ms. G Janani.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: The study of this work aims to create a portable, low-cost urine analysis system to predict the risk of kidney stones earlier by using integrated sensors and machine learning models. Materials and Methods: In this research, we consider two groups. Group 1 represents the conventional supervised machine learning approaches (Support Vector Machine, Random Forest) using 100 test samples and Group 2 uses XGBoost model trained using larger urine analysis dataset which contains over 300 samples to improve the performance. Result: The XGBoost reached an overall prediction accuracy of about 96%. It also showed consistent model performance with significance value of p=0.018. Conclusion: A portable urine analysis system was successfully developed with multiple sensors and machine learning to predict kidney stone risk early. The proposed approach offers a low-cost solution that can help with preventive kidney stone monitoring, particularly in clinics and healthcare settings with limited resources
Disclaimer: Curated by HT Syndication.
