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Predictive Risk Models (Healthcare)
ML & AIClassification models predicting health outcomes including diabetes risk and A&E wait time predictions.
Overview
💡 Challenge
Healthcare providers need early identification of high-risk patients and better resource allocation for emergency departments.
⚡ Solution
Developed gradient boosting models using clinical and demographic data to predict diabetes risk and emergency wait times.
🎯 Impact
Enables preventive care targeting and improved ED resource planning.
Technical Details
🛠️ Tech Stack
PythonXGBoostScikit-learnSHAPStreamlit
✨ Key Features
- ✓Diabetes risk prediction with feature importance
- ✓A&E wait time forecasting
- ✓SHAP explainability for clinical trust
- ✓Risk stratification dashboards
Key Learnings
- →Healthcare ML requires explainability for clinical adoption
- →Dealing with missing data is critical in health records
- →Model fairness across demographic groups must be validated
📊 Data Notes
This project uses synthetic/open data to demonstrate capabilities while maintaining privacy and confidentiality. All methods and approaches are applicable to real-world scenarios.