Abstract
Huntington’s disease (HD) is an inherited neurodegenerative disorder causing problems with mobility, cognition and mood. Gait abnormality is a potential diagnostic sign as it can occur even in the early stages of HD. We developed a machine learning method for detecting HD with gait dynamics as the model features. Concretely, standard deviation (SD) and interquartile range (IQR) were calculated for 6 gait time series sequences as 12 candidate features. An exhaustive feature and hyperparameter selector was then applied to optimize the features and hyperparameter subsets for 5 different machine learning models. Classification outcomes were determined by nested leave-one-out cross-validation (nested LOOCV) method. Support Vector Machines (SVM) achieved the highest accuracy (97.14%) without overfitting bias assumptions. Our result showed that the machine learning based method with gait dynamics features can be a complementary tool for HD diagnosis.
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Huang, X., Khushi, M., Latt, M., Loy, C., Poon, S.K. (2019). Machine Learning Based Method for Huntington’s Disease Gait Pattern Recognition. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_66
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DOI: https://doi.org/10.1007/978-3-030-36808-1_66
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