2024
DOI: 10.1038/s41598-024-56656-4
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Identification and interpretation of gait analysis features and foot conditions by explainable AI

Mustafa Erkam Özateş,
Alper Yaman,
Firooz Salami
et al.

Abstract: 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 recur… Show more

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