Machine Learning and Statistical Approaches to Support the Discrimination of Neuro-degenerative Diseases Based on Gait Analysis
Amyotrophic lateral sclerosis, Parkinson’s disease and Huntington’s disease are three neuro-degenerative diseases. In all these diseases, severe disturbances of gait and gait initiation are frequently reported. In this paper, we explore the feasibility of using machine learning and statistical approaches to support the discrimination of these three diseases based on gait analysis. A total of three supervised classification methods, namely support vector machine, KStar and Random Forest, were evaluated on a publicly-available gait dataset. The results demonstrate that it is feasible to apply computational classification techniques in characterise these three diseases with the features extracted from gait cycles. Results obtained show that using selected 4 features based on maximum relevance and minimum redundancy strategy can achieve reasonably high classification accuracy while 5 features can achieve the best performance. The continual increase of the number of features does not significantly improve classification performance.
Keywordsclassification feature selection neuro-degenerative diseases
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- 1.ALS Association: What is ALS? (September 10, 2007), http://www.alsa.org/als/what.cfm
- 3.Glodfarb, B.J., Simon, S.R.: Gait analysis in patients with amyotrophic lateral sclerosis. Arch. Phys. Med. rehabi. 65, 61–65 (1984)Google Scholar
- 4.Hausdorff, J.M., Lertratanakul, A., Cudkowicz, M.E., Peterson, A.L., Kaliton, D., Goldberger, A.L.: Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J. Appl. Physiol. 88, 2045–2053 (2000)Google Scholar
- 8.Prentice, S., Patla, A.E.: Modelling of some aspects of skilled locomotor behaviour using artificial neural networks. In: Begg, R., Palaniswami, M. (eds.) Computational Intelligence for Movement Sciences. IDEA group publishing (2006)Google Scholar
- 11.Begg, R., Palaniswami, M.: Recognition of gait patterns using support vector machines. In: Begg, R., Palaniswami, M. (eds.) Computational Intelligence for Movement Sciences: Neural Networks and other Emerging Techniques. IDEA group publishing (2006)Google Scholar
- 12.Mierswa, M., Wurst, R., Klinkenberg, M., Scholz, Timm, E.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006) (2006) Google Scholar
- 13.Rose, J., Gamble, J.G.: Human Walking. Williams & Wilkins, London (1994)Google Scholar
- 14.Yuan, H., Tseng, S.-S., Gangshan, W., Fuyan, Z.: A two-phase feature selection method using both filter and wrapper. In: Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, vol. 2, pp. 132–136. IEEE Computer Society Press, Piscataway (1999)Google Scholar
- 16.Hausdorff, J.M., Mitchell, S.L., Firtion, R., Peng, C.K., Cudkowicz, M.E., Wei, J.Y., Goldberger, A.L.: Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. J. Applied Physiology 82, 262–269 (1997)Google Scholar
- 17.Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23), e215–e220 (2000); Circulation Electronic Pages, http://circ.ahajournals.org/cgi/content/full/101/23/e215 Google Scholar