ZURE-BASED HEALTHCARE RISK ANALYTICS WITH MACHINE LEARNING AND POWER B

A Cloud-Native Healthcare Analytics Workflow Using Azure Services and Power BI for Patient Risk Categorization is designed to help hospitals centrally monitor and manage high-risk patients using a fully implemented cloud architecture. The system ingests patient vital signs and related clinical data from multiple hospital branches (initially stored in Excel or similar sources) into Azure through automated data pipelines, where the data is cleaned, validated, and organized using a star-schema model to support efficient analytics and reporting. On top of this curated dataset, the workflow applies rule-based and machine learning driven risk logic to classify patients into risk categories, enabling early identification of deteriorating cases and prioritization of critical care. Azure SQL and related services provide secure, scalable storage for protected health information, while Power BI delivers interactive dashboards that surface real-time risk scores, trends, and alerts to clinicians and administrators, reducing reliance on manual spreadsheet tracking and fragmented reporting. By operationalizing patient risk stratification on a single Azure platform, the project demonstrates how cloud-native analytics, integrated governance, and automated notifications can improve decision-making speed, support proactive interventions for high-risk patients, and lay a practical foundation for future extensions such as streaming loT vitals and advanced predictive models.