Analyzing Feature Subsets in a Machine Learning Model to Aid AI Explainability
Abstract
Machine learning algorithms are becoming more used in the area of medicine because they can find patterns in complicated medical data. The active use of a mostly black box is a contentious issue, especially in this sensitive sector. We want to show how an aggregated and systematic feature analysis of such models might be useful in a medical setting. As a result, we present a grouped version of the permutation significance analysis to assess the impact of complete feature subsets in a machine learning model. Expert-defined subgroups can thus be examined in the decision-making process. Based on these findings, new theories can be developed and tested.
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