Healthcare Machine Learning Project
01
Interpretable Machine Learning and Causal Inference in Cervical Cancer Diagnosis
[Scikit-learn, PyTorch, SHAP, Skater, Tetrad]
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Developed a machine learning-based digital screening solution for cervical cancer detection in underserved areas, incorporating advanced causal inference and machine learning techniques to enhance the interpretability and explainability of predictions
02
Decision Support System Prototype to Prevent Nurse Burnout
[Python, SQL, Microsoft Excel, Microsoft Access]
Developed a decision support system prototype to predict and prevent nurse burnout, incorporating a relational database, a logistic regression model with 72% accuracy, and a user interface for HR management teams
03
Revolutionizing Healthcare: Harnessing Artificial Intelligence and ArcGIS to Address Industry Challenges and Improve Care Delivery
[ArcGIS Pro, Scikit-learn]
Developed analytics frameworks for proactive mental health crisis prediction, resource allocation, and chronic disease management using non-clinical data
04
Food Basket Optimization for Nigerian Households
[Scikit-learn, Gurobi™ Optimizer]
Developed random forest and K-means clustering models to predict food insecurity levels and group similar Nigerian households; designed optimized, nutritionally balanced and affordable food baskets tailored to different household types
