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@article{177204,
        author = {Srichandana Siramdasu and Mahi Verma and N.Akshitha and S.Hitheesha and T.Vishlesh},
        title = {Health Monitoring Dashboard},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {926-931},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177204},
        abstract = {This paper introduces a machine learning (ML)-powered medical monitoring dashboard aimed at improving patient care through real-time data analysis. The system continuously tracks key health indicators such as heart rate, blood pressure, oxygen saturation, and body temperature. Using advanced ML algorithms, it detects abnormalities, forecasts potential health declines, and provides early warnings. By leveraging predictive models and anomaly detection techniques, the dashboard equips healthcare professionals with data-driven insights, facilitating timely interventions and improving clinical decision-making. This approach enhances the efficiency of patient monitoring across both critical and routine healthcare scenarios.},
        keywords = {Medical Monitoring, Machine Learning, Health Forecasting, AI in Healthcare, Healthcare Dashboard, Predictive Analytics},
        month = {May},
        }