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Identifying Parkinson’s Patients: A Functional Gradient Boosting Approach

  • Devendra Singh DhamiEmail author
  • Ameet Soni
  • David Page
  • Sriraam Natarajan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

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 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Devendra Singh Dhami
    • 1
    Email author
  • Ameet Soni
    • 2
  • David Page
    • 3
  • Sriraam Natarajan
    • 1
  1. 1.Indiana University BloomingtonBloomingtonUSA
  2. 2.Swarthmore CollegeSwarthmoreUSA
  3. 3.University of Wisconsin-MadisonMadisonUSA

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