MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541124126 A) filed by B V Raju Institute Of Technology, Narsapur, Telangana, on Dec. 9, 2025, for 'smart healthcare decision support system using machine learning models.'
Inventor(s) include Dr. L T Hemalatha; Kotha Chandrakala; Dr. S. Saravanan; Sai Shivani; and M. Bhavanesh.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "The present invention discloses a Smart Healthcare Decision Support System that employs machine learning techniques to enhance clinical diagnosis, medication selection, and patient-doctor communication within a unified digital framework. The system enables patients to input symptoms through an interactive interface, after which the backend processes the information and applies trained machine learning models to determine probable diseases and recommend suitable drugs. These predictions are securely transmitted to a dedicated doctor portal, where healthcare professionals review and validate the results before finalizing treatment decisions.The invention integrates multiple clinical functions-symptom collection, automated disease prediction, drug recommendation, medical review, and appointment scheduling-into a single platform, thereby improving the speed and reliability of healthcare delivery. The backend architecture supports structured data preprocessing, model execution, and secure communication between the patient and doctor interfaces. Interpretable algorithms ensure that clinicians can understand and verify the reasoning behind predictions, strengthening trust and supporting evidence-based decision making.Designed for scalability, the system can be deployed in hospitals, clinics, or telemedicine settings, making it particularly beneficial for regions with limited medical resources. By reducing delays associated with fragmented digital tools and manual processes, the invention facilitates early diagnosis, improves treatment accuracy, and promotes accessible healthcare services. The proposed system thus offers an efficient, integrated, and clinically supportive solution for modern healthcare environments."
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