Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{192148,
        author = {Mayank Sharma and Vipasha Samal and Ankita Dubey and Anchal Maurya and Mrs. Pooja Singh},
        title = {Cardio Scan AI: A Hybrid Machine Learning Approach for Early Prediction of Cardiac Risk and Personalized Preventive Care},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {520-524},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192148},
        abstract = {Heart diseases continue to be one of the main causes of death across the world, largely because many of them develop quietly without noticeable symptoms until they become serious. Early detection and preventive intervention are essential for reducing the associated health risks. This study introduces Cardio Scan AI, a hybrid intelligent framework designed to predict heart disease risk using easily obtainable clinical and lifestyle parameters. The system integrates ensemble machine learning with a deep neural network to analyze multiple factors, including blood pressure, cholesterol, BMI, physical activity, and stress levels, allowing accurate and personalized risk assessment. In addition to prediction, the framework features a personalized prevention module that provides tailored lifestyle and clinical recommendations based on individual risk profiles. Experimental evaluation on a dataset of 2,300 samples demonstrated that Cardio Scan AI achieves high predictive performance, with an accuracy of 94.2% and ROC–AUC of 0.96, outperforming traditional machine learning models. The results highlight the framework’s potential for accessible, reliable, and proactive cardiac health management, bridging the gap between early detection and personalized preventive care.},
        keywords = {},
        month = {February},
        }