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

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.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Armañanzas R,Bielza C,Chaudhuri KR,Martinez-Martin P,Larrañaga P

doi

10.1016/j.artmed.2013.04.002

subject

Has Abstract

pub_date

2013-07-01 00:00:00

pages

195-202

issue

3

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(13)00054-7

journal_volume

58

pub_type

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