MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202621048495 A) filed by Moresh Madhukar Mukhedkar; Nikhil Mahale; Shweta Koparde; Vivek Patil; Prof. Anurag Kumar; Abhishek Kurhade; Shreyash Jadhav; and Vaishnavi Nichit on April 16, 2026, for Evaluating Machine Learning Classifiers For Ranking Post Vaccination Cardiovascular Risk.

Inventors include Moresh Madhukar Mukhedkar; Nikhil Mahale; Shweta Koparde; Vivek Patil; Prof. Anurag Kumar; Abhishek Kurhade; Shreyash Jadhav; and Vaishnavi Nichit.

The application for the patent was published on June 12, 2026, under issue no. 24/2026.

Abstract: The present invention relates to a Evaluating Machine Learning Classifiers For Ranking Post Vaccination Cardiovascular Risk, a machine learning-based computer- implemented system designed to predict the likelihood of cardiovascular disease in individuals based on patient health parameters. The system accepts demographic, physiological, and lifestyle data — including age, gender, cholesterol levels, blood pressure, BMI, heart rate, glucose level, and smoking habits — and processes them through a data preprocessing pipeline comprising feature selection, missing value imputation, and standardization. A predictive analytics engine employing Decision Tree, Random Forest, and Logistic Regression classifiers is trained on a structured cardiovascular disease dataset. The Random Forest Classifier demonstrated superior predictive accuracy. The system generates a binary risk prediction (0 = No Heart Disease Risk, 1 = Heart Disease Risk) and displays prediction confidence to assist healthcare professionals in clinical decision-making. The trained model is stored using Joblib for scalable API-based deployment in hospitals, telemedicine platforms, health monitoring applications, and preventive healthcare systems. The invention demonstrates the potential of machine learning in early cardiovascular disease detection, risk assessment, and healthcare outcome improvement.

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