Unveiling relevant non-motor Parkinson’s disease severity symptoms using a machine learning approach

Armañanzas Arnedillo, Ruben and Bielza Lozoya, María Concepción and Chaudhuri, Karoll R. and Martínez-Martín, Pablo and Larrañaga Múgica, Pedro María (2013). Unveiling relevant non-motor Parkinson’s disease severity symptoms using a machine learning approach. "Artificial Intelligence in Medicine", v. 58 (n. 3); pp. 195-202. ISSN 1873-2860. https://doi.org/10.1016/j.artmed.2013.04.002.

Description

Title: Unveiling relevant non-motor Parkinson’s disease severity symptoms using a machine learning approach
Author/s:
  • Armañanzas Arnedillo, Ruben
  • Bielza Lozoya, María Concepción
  • Chaudhuri, Karoll R.
  • Martínez-Martín, Pablo
  • Larrañaga Múgica, Pedro María
Item Type: Article
Título de Revista/Publicación: Artificial Intelligence in Medicine
Date: July 2013
ISSN: 1873-2860
Volume: 58
Subjects:
Freetext Keywords: Estimation of distribution algorithms, Feature subset selection, Severity indexes, Parkinson’s disease
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Objective: Is it possible to predict the severity staging of a Parkinson’s disease (PD) patient using scores of non-motor symptoms? This is the kickoff question for a machine learning approach to classify two widely known PD severity indexes using individual tests from a broad set of non-motor PD clinical scales only.

Methods: The Hoehn & Yahr index and clinical impression of severity index are global measures of PD severity. They constitute the labels to be assigned in two supervised classification problems using only non-motor symptom tests as predictor variables. Such predictors come from a wide range of PD symptoms, such as cognitive impairment, psychiatric complications, autonomic dysfunction or sleep disturbance. The classification was coupled with a feature subset selection task using an advanced evolutionary algorithm, namely an estimation of distribution algorithm. Results: Results show how five different classification paradigms using a wrapper feature selection scheme are capable of predicting each of the class variables with estimated accuracy in the range of 72–92%. In addition, classification into the main three severity categories (mild, moderate and severe) was split into dichotomic problems where binary classifiers perform better and select different subsets of non-motor symptoms. The number of jointly selected symptoms throughout the whole process was low, suggesting a link between the selected non-motor symptoms and the general severity of the disease.

Conclusion: Quantitative results are discussed from a medical point of view, reflecting a clear translation to the clinical manifestations of PD. Moreover, results include a brief panel of non-motor symptoms that could help clinical practitioners to identify patients who are at different stages of the disease from a limited set of symptoms, such as hallucinations, fainting, inability to control body sphincters or believing in unlikely facts.

Funding Projects

Type
Code
Acronym
Leader
Title
Government of Spain
TIN2010-20900- C04-04
Unspecified
Unspecified
Unspecified
Government of Spain
Unspecified
Unspecified
Unspecified
Cajal Blue Brain

More information

Item ID: 72859
DC Identifier: https://oa.upm.es/72859/
OAI Identifier: oai:oa.upm.es:72859
DOI: 10.1016/j.artmed.2013.04.002
Official URL: https://www.sciencedirect.com/science/article/pii/...
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 17 Mar 2023 11:45
Last Modified: 17 Mar 2023 11:45
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