Identifying Parkinson's Patients: A Functional Gradient Boosting Approach.

Authors:
Ameet Soni
Ameet Soni
University of Wisconsin
United States
David Page
David Page
Whitehead Institute
United States
Sriraam Natarajan
Sriraam Natarajan
Wake Forest University

Artif Intell Med (2017) 2017 Jun 30;10259:332-337. Epub 2017 May 30.

Indiana University Bloomington.

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 Parkinsons 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 Parkinsons Progression Markers Initiative and show that it achieves a state-of-the-art performance with minimal feature engineering.

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Source
http://dx.doi.org/10.1007/978-3-319-59758-4_39DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5653963PMC

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June 2017
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