Machine Learning for Health Informatics pp 209-220

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9605) | Cite as

Machine Learning and Data Mining Methods for Managing Parkinson’s Disease

  • Dragana Miljkovic
  • Darko Aleksovski
  • Vid Podpečan
  • Nada Lavrač
  • Bernd Malle
  • Andreas Holzinger
Chapter

Abstract

Parkinson’s disease (PD) results primarily from dying of dopaminergic neurons in the Substantia Nigra, a part of the Mesencephalon (midbrain), which is not curable to date. PD medications treat symptoms only, none halt or retard dopaminergic neuron degeneration. Here machine learning methods can be of help since one of the crucial roles in the management and treatment of PD patients is detection and classification of tremors. In the clinical practice, this is one of the most common movement disorders and is typically classified using behavioral or etiological factors. Another important issue is to detect and evaluate PD related gait patterns, gait initiation and freezing of gait, which are typical symptoms of PD. Medical studies have shown that 90% of people with PD suffer from vocal impairment, consequently the analysis of voice data to discriminate healthy people from PD is relevant. This paper provides a quick overview of the state-of-the-art and some directions for future research, motivated by the ongoing PD_manager project.

Keywords

Machine learning Data mining Parkinson’s disease 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Dragana Miljkovic
    • 1
  • Darko Aleksovski
    • 1
  • Vid Podpečan
    • 1
  • Nada Lavrač
    • 1
  • Bernd Malle
    • 2
  • Andreas Holzinger
    • 2
    • 3
  1. 1.Knowledge Technologies DepartmentJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Holzinger Group, Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria
  3. 3.Institute of Information Systems and Computer MediaGraz University of TechnologyGrazAustria

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