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@article{196220,
        author = {Mangali Divya and K.Ajay Chowdary and Karingula Navya and Vinayak G Biradar},
        title = {MedGuardZKP– A Privacy Preserving And AI-driven Framework for Healthcare Data Management System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2881-2888},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196220},
        abstract = {The rapid advancement of intelligent data-driven technologies has significantly transformed healthcare by enabling continuous monitoring and early detection of critical conditions. This paper presents a comprehensive healthcare monitoring framework that integrates machine learning-based anomaly detection with explainable analytics for real-time patient risk assessment. The proposed system employs the Isolation Forest algorithm to identify abnormal patterns in vital physiological parameters such as heart rate, body temperature, and oxygen saturation levels, and classifies patient conditions into Normal, Moderate Risk, and High-Risk categories. The framework is designed to support efficient real-time processing while ensuring scalability for large and complex healthcare datasets.
To enhance transparency and clinical usability, the system incorporates rule-based reasoning to generate interpretable in- sights that explain detected anomalies. This enables healthcare professionals to better understand patient conditions and make informed decisions. By combining intelligent anomaly detection with explainable outputs, the proposed approach facilitates timely interventions and strengthens clinical decision support, ultimately contributing to improved patient care and more reliable health- care monitoring systems.},
        keywords = {Healthcare Monitoring, Anomaly Detection, Isolation Forest, Explainable AI, Machine Learning, Real-Time Systems, Patient Risk Assessment, Clinical Decision Support},
        month = {April},
        }