Identifying Parkinson’s Patients: A Functional Gradient Boosting Approach

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...

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Bibliographic Details
Published inArtificial Intelligence in Medicine Vol. 10259; pp. 332 - 337
Main Authors Dhami, Devendra Singh, Soni, Ameet, Page, David, Natarajan, Sriraam
Format Book Chapter Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.06.2017
SeriesLecture Notes in Computer Science
Subjects
Functional gradient boosting
Human advice
Parkinson’s
Functional gradient boosting
human advice
Parkinson’s
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Summary: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.
ISBN:9783319597577
3319597574
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-59758-4_39