Identification and interpretation of gait analysis features and foot conditions by explainable AI

Clinical gait analysis is a crucial step for identifying foot disorders and planning surgery. Automating this process is essential for efficiently assessing the substantial amount of gait data. In this study, we explored the potential of state-of-the-art machine learning (ML) and explainable artific...

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Bibliographic Details
Published in:Scientific reports 2024-03, Vol.14 (1), p.5998-5998, Article 5998
Main Authors: Özateş, Mustafa Erkam, Yaman, Alper, Salami, Firooz, Campos, Sarah, Wolf, Sebastian I., Schneider, Urs
Format: Article
Language:English
Subjects:
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Algorithms
Artificial Intelligence
Automation
Feet
Foot
Gait
Gait Analysis
Humanities and Social Sciences
Machine Learning
multidisciplinary
Science
Science (multidisciplinary)
Support vector machines
Surgery
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Summary:Clinical gait analysis is a crucial step for identifying foot disorders and planning surgery. Automating this process is essential for efficiently assessing the substantial amount of gait data. In this study, we explored the potential of state-of-the-art machine learning (ML) and explainable artificial intelligence (XAI) algorithms to automate all various steps involved in gait analysis for six specific foot conditions. To address the complexity of gait data, we manually created new features, followed by recursive feature elimination using Support Vector Machines (SVM) and Random Forests (RF) to eliminate low-variance features. SVM, RF, K-nearest Neighbor (KNN), and Logistic Regression (LREGR) were compared for classification, with a Majority Voting (MV) model combining trained models. KNN and MV achieved mean balanced accuracy, recall, precision, and F1 score of 0.87. All models were interpreted using Local Interpretable Model-agnostic Explanation (LIME) method and the five most relevant features were identified for each foot condition. High success scores indicate a strong relationship between selected features and foot conditions, potentially indicating clinical relevance. The proposed ML pipeline, adaptable for other foot conditions, showcases its potential in aiding experts in foot condition identification and planning surgeries.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-56656-4