Explaining Machine Learning Models for Clinical Gait Analysis

Machine Learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, their black-box character. This article investigates the usefulness of Expla...

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Published in:ACM transactions on computing for healthcare 2022-04, Vol.3 (2), p.1-27, Article 14
Main Authors: Slijepcevic, Djordje, Horst, Fabian, Lapuschkin, Sebastian, Horsak, Brian, Raberger, Anna-Maria, Kranzl, Andreas, Samek, Wojciech, Breiteneder, Christian, Schöllhorn, Wolfgang Immanuel, Zeppelzauer, Matthias
Format: Article
Language:English
Subjects:
Applied computing
Computing methodologies
Health care information systems
Neural networks
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Summary:Machine Learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, their black-box character. This article investigates the usefulness of Explainable Artificial Intelligence (XAI) methods to increase transparency in automated clinical gait classification based on time series. For this purpose, predictions of state-of-the-art classification methods are explained with a XAI method called Layer-wise Relevance Propagation (LRP). Our main contribution is an approach that explains class-specific characteristics learned by ML models that are trained for gait classification. We investigate several gait classification tasks and employ different classification methods, i.e., Convolutional Neural Network, Support Vector Machine, and Multi-layer Perceptron. We propose to evaluate the obtained explanations with two complementary approaches: a statistical analysis of the underlying data using Statistical Parametric Mapping and a qualitative evaluation by two clinical experts. A gait dataset comprising ground reaction force measurements from 132 patients with different lower-body gait disorders and 62 healthy controls is utilized. Our experiments show that explanations obtained by LRP exhibit promising statistical properties concerning inter-class discriminativity and are also in line with clinically relevant biomechanical gait characteristics.
ISSN:2691-1957
2637-8051
DOI:10.1145/3474121