Generating Counterfactual Explanations for Electrocardiography Classification with Native Guide
Explanations are essential components in the promising fields of artificial intelligence (AI) and machine learning. Deep learning approaches are rising due to their supremacy in terms of accuracy when trained with huge amounts of data. Because of their black-box nature, the predictions are also hard to comprehend, retrace, and trust. Good explanation techniques can help to understand why a system produces a certain prediction and therefore increase trust in the model. Understanding the model is crucial for domains like healthcare, where decisions ultimately affect human life. Studies have shown that counterfactual explanations in particular tend to be more informative and psychologically effective than other methods.
This work focuses on a novel instance-based technique called “Native Guide”, that generates counterfactual explanations for time series data classification. It uses nearest neighbour samples from the real data distribution with class change as a foundation. This thesis applies the method on the explanation of electrocardiogram (ECG) classification, a very complex and vital medical field where every single ecg carries unique features. Native
Guide for ECGs is explained, examined and expanded by providing necessary background knowl edge, amplifying aspects like plausibility, comparing different suitable models to each other and indicating benefits and downsides. Finally, counterfactual explanations for ecg data classification generated by Native Guide are evaluated by cardiologists by means of two expert interviews.
Synchronization of the periodic ecg data was shown to be the most important contribution to the method that enabled the generation of plausible coun- terfactuals. The experts, who had never seen or used counterfactuals in their work, were interested in this approach and could envision its application within the field when it comes to training junior doctors. In general, AI classification along with sophisticated proximate counterfactuals indicate success and reliability when it comes to the identification of heart diseases.