International Journal of Science, Engineering and TechnologyAuthors: Dr Raj Kumar, Aarchi, Karuna Rajput, Shagun Kamboj
Abstract: The rapid rise in digital health records has sparked a strong need for smart tools that convert unprocessed body data into practical medical decisions. This study introduces the Health Monitoring Dashboard (HMD), a unified web app combining data analysis, visual interaction, and machine learning predictions. Developed using Python (v3.x), the platform relies on Streamlet for real-time responses, Plotly Express for rich charts, Pandas for efficient data handling, and Joblib to manage trained models. The interface features six components: filtering user conditions, aggregating performance metrics, displaying time-based trends and cross-data relationships, applying rule-driven health rules, and offering a machine learning testing environment. Tests confirm the system accurately handles inputs such as heart rate, blood pressure, sleep habits, steps taken, and physical activity with live updates achieved in less than a second on typical personal computers. Future improvements include deeper ML model support through Joblib workflows, connecting directly to fitness devices like Fitbit or Garmin, deploying securely on cloud services with backend databases, and enabling multi-user access based on roles. The results show that accessible Python tools can build reliable healthcare analytics systems and offer a replicable blueprint for others
DOI: http://doi.org/
