Intellectual Property India Publishes Patent Application for Integrated Machine Learning And Iot-Cloud Framework For Early Cardiovascular Disease Detection And Targeted Nanoparticle Therapy

MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641060126 A) filed by Sathesh Kumar Sukumaran; Hanish Singh Jayasingh Chellammal; Dr. Bonam Raghavaiah; Katha. Chandrashekhar; Nurjabova Dilafruz; Dr. S. Swetha; Kiruthika J K; Dr A Udhayakumar; Dr. C. Venkatesh; K. V. Priyadharshini; Santosh R Mitekar; and Dr Akash Biradar on May 12, 2026, for Integrated Machine Learning And Iot-Cloud Framework For Early Cardiovascular Disease Detection And Targeted Nanoparticle Therapy.

Inventors include Sathesh Kumar Sukumaran; Hanish Singh Jayasingh Chellammal; Dr. Bonam Raghavaiah; Katha. Chandrashekhar; Nurjabova Dilafruz; Dr. S. Swetha; Kiruthika J K; Dr A Udhayakumar; Dr. C. Venkatesh; K. V. Priyadharshini; Santosh R Mitekar; and Dr Akash Biradar.

The application for the patent was published on June 19, 2026, under issue no. 25/2026.

Abstract: Integrated Machine Learning and IoT-Cloud Framework for Early Cardiovascular Disease Detection and Targeted Nanoparticle Therapy is the proposed invention. The proposed invention is designed to provide continuous real-time monitoring, intelligent disease prediction, and personalized treatment support for patients suffering from cardiovascular disorders. IoT-enabled wearable sensors are utilized to collect physiological parameters such as heart rate, electrocardiogram signals, blood pressure, oxygen saturation, cholesterol levels, and body temperature. The collected healthcare data is securely transmitted to a cloud-based platform for storage, preprocessing, and analytical processing. The framework employs the Random Forest machine learning algorithm to analyze real-time and historical patient data for identifying cardiovascular abnormalities and predicting disease risk levels with improved accuracy. The algorithm generates multiple decision trees to enhance classification performance and reduce prediction errors. When abnormal conditions are detected, the system automatically sends alerts and diagnostic recommendations to healthcare professionals and patients for timely medical intervention. The cloud environment supports scalable data management, remote healthcare accessibility, and continuous synchronization between medical devices and healthcare providers.

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