Title

Identifying Parkinson’s Patients: A Functional Gradient Boosting Approach

Document Type

Conference Proceeding

Publication Date

2017

Published In

Artificial Intelligence In Medicine

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.

Keywords

functional gradient boosting, Parkinson's, human advice

Published By

Springer

Editor(s)

A. ten Teije, C. Popow, J. H. Holmes, And L. Sacchi

Conference

16th Conference On Artificial Intelligence In Medicine

Conference Dates

June 21-24, 2017

Conference Location

Vienna, Austria