Intellectual Property India Publishes Patent Application for An Intelligent Healthcare Decision Support System Using Ensemble Machine Learning Models For Real-Time Detection And Monitoring Of Heart Disease Risk With Data Security Framework

MUMBAI, India, June 30 -- Intellectual Property India has published a patent application (202641073868 A) filed by Sr University on June 15, 2026, for An Intelligent Healthcare Decision Support System Using Ensemble Machine Learning Models For Real-Time Detection And Monitoring Of Heart Disease Risk With Data Security Framework.

Inventors include Chilukuri Sujatha; and Dr Ramesh Babu A.

The application for the patent was published on June 26, 2026, under issue no. 26/2026.

Abstract: The present invention relates to a secure healthcare decision support system for early detection of heart diseases using hybrid machine learning models. The system integrates heterogeneous healthcare data obtained from electronic health records, wearable sensor devices, laboratory reports, diagnostic imaging systems, and patient lifestyle records to generate accurate cardiovascular risk predictions. A data acquisition module collects and aggregates multimodal patient information, which is then processed through a preprocessing unit that performs cleaning, normalization, noise reduction, and feature standardization to ensure high-quality analytical input.A hybrid machine learning engine is employed, combining multiple predictive techniques such as ensemble learning, deep neural networks, recurrent neural networks, and transformer-based architectures to capture both nonlinear relationships and temporal dependencies in cardiovascular health data. The system further incorporates multimodal data fusion to unify structured and unstructured medical information into a coherent predictive representation.A real-time inference engine continuously analyzes incoming patient data to generate dynamic risk scores and early warning alerts for potential heart disease conditions. An explainable artificial intelligence module provides transparent and interpretable insights into prediction outcomes by identifying key contributing clinical factors. Additionally, a security and privacy framework ensures encrypted data handling, secure authentication, and compliance with healthcare regulations. The proposed system enhances diagnostic accuracy, enables early intervention, improves clinical decision-making, and supports scalable deployment

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