Research: Evaluating SHAP and Counterfactual Explanations for Interpretability in Clini...

Research question: How do SHAP and counterfactual analysis methods improve interpretability and trustworthiness of clinical risk prediction models compared to traditional feature importance techniques?

Background & Motivation: Clinical risk models powered by machine learning are increasingly used to inform healthcare decisions, yet their complex, often opaque nature can hinder adoption by clinicians and patients.

Research Gap: Existing interpretability methods, such as feature importance rankings, frequently fail to provide nuanced, actionable explanations for individual predictions, limiting their practical utility in clinical settings.

Approach & Expected Contribution: This research will systematically evaluate SHAP (Shapley Additive Explanations) and counterfactual analysis as tools for providing transparent, patient-specific explanations in established clinical risk models (e.g., for cardiac events or diabetes risk), using publicly available datasets. Comparative analysis will investigate their effectiveness relative to conventional interpretability techniques.

Significance: Improved explainability can enhance clinician trust, facilitate regulatory approval, and lead to better patient outcomes by enabling clearer, more actionable risk communication.