Evaluating Hierarchical Medical Workflows using Feature Importance
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Our work on utilising hierarchical medical workflow for understanding the operation of ML in a healthcare-based setting was presented by Urja Pawar at IEEE CBMS 2021. The utility of the approach is demonstrated in the context of heart disease classification. Explainable Artificial Intelligence (XAI) is incorporated in the form of Feature Importance scores and correlated with an ML model’s performance metrics (Accuracy, F1-score). This provides a multi-stakeholder perspective aligned with the hierarchy as experienced in a real-world medical setting. The work contributes a methodology for accommodating an enhanced understanding of diverse hierarchical healthcare settings that would benefit from the adoption of AI-based systems.