Abstract
Parkinson’s, a progressive neural disorder, is difficult to identify due to the hidden nature of the symptoms associated. We present a machine learning approach that uses a definite set of features obtained from the Parkinson’s Progression Markers Initiative (PPMI) study as input and classifies them into one of two classes: PD (Parkinson’s disease) and HC (Healthy Control). As far as we know this is the first work in applying machine learning algorithms for classifying patients with Parkinson’s disease with the involvement of domain expert during the feature selection process. We evaluate our approach on 1194 patients acquired from Parkinson’s Progression Markers Initiative and show that it achieves a state-of-the-art performance with minimal feature engineering.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Marek, K., et al.: The parkinson progression marker initiative (PPMI). Prog. Neurobiol. 95(4), 629–635 (2011)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Blockeel, H., De Raedt, L.: Top-down induction of first-order logical decision trees. Artif. Intell. 101(1), 285–297 (1998)
Goetz, C.G., et al.: Movement Disorder Society sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDSUPDRS): process, format, and clinimetric testing plan. Mov. Disord. 22(1), 41–47 (2007)
Craven, M.W., Jude, W.S.: Extracting tree-structured representations of trained networks. In: Advances in Neural Information Processing Systems (1996)
Natarajan, S., et al.: Early prediction of coronary artery calcification levels using machine learning. In: IAAI (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Dhami, D.S., Soni, A., Page, D., Natarajan, S. (2017). Identifying Parkinson’s Patients: A Functional Gradient Boosting Approach. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-59758-4_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59757-7
Online ISBN: 978-3-319-59758-4
eBook Packages: Computer ScienceComputer Science (R0)