This project proposes an AI-powered ICU mortality prediction system using the MIMIC-IV dataset. The system analyzes patient demographics, vital signs, laboratory results, and ICU admission data to identify high-risk patients.
Machine learning models such as XGBoost and Random Forest will be used to predict mortality risk. Explainable AI techniques will help clinicians understand the factors influencing predictions.
An interactive Power BI dashboard will provide insights into patient risk levels, mortality trends, ICU stay duration, and clinical indicators. The goal is to support early intervention, improve resource allocation, and enhance patient outcomes through data-driven decision making.
Technologies: Python, Pandas, Scikit-learn, XGBoost, Power BI, MIMIC-IV Dataset.
Expected Outcome: Accurate mortality prediction, interpretable risk analysis, and a healthcare analytics dashboard for ICU patient monitoring.
