Abdul Raheem Muhammed Ramadan

Built a complete Hospital Mortality Analysis workbook around a six-stage analytics framework (Discovery, Exploration, Modelling, Projection, Actionable Insight, Monitoring), combining risk-adjusted statistical testing, an interactive PivotChart dashboard, a Monte Carlo simulation with a what-if control panel, and a KPI monitoring framework with escalation thresholds — everything a CMO needs to move from a raw number to a defensible decision.

Built a normalised MySQL schema and a Python statistics + machine learning notebook that decompose feeder-level ATC&C (Aggregate Technical, Commercial & Collection) losses, validate patterns with hypothesis tests and effect sizes, and train a Random Forest model to identify the dominant loss driver.

Built and deployed a logistic regression model that predicts heart disease risk from 13 clinical features, with a Streamlit interface for interactive risk assessment and a FastAPI backend for programmatic access.

Built a classification model to predict patient no-shows, enabling targeted SMS reminders that could reduce missed appointments by up to 30%.

Analysed A&E attendance data to identify temporal patterns in wait times, helping trusts allocate staff more effectively during peak periods.

Analysed synthetic medical claims data to identify patterns in denial rates by payer, procedure, and submission timing, producing actionable recommendations for revenue cycle teams.