AI-Powered Patient Risk Prioritization

Developed a machine learning-based real-time patient risk prioritization system, integrating clinical and

demographic data.

• Implemented Random Forests, Gradient Boosting, and Support Vector Machines to accurately classify risk

levels (Low, Medium, High).

• Engineered features from vital signs, patient demographics, and alert history, optimizing prediction

performance.

• Designed an interactive Gradio-based UI, enabling medical professionals to efficientlyassess .