title: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Campus de Montegancedo, 28660 Boadilla del Monte, Spain. r.armananzas@upm.es
authors.forename: Rubén
authors.initials: R
authors.lastname: Armañanzas
authors.forename: Concha
authors.initials: C
authors.lastname: Bielza
authors.forename: Kallol Ray
authors.initials: KR
authors.lastname: Chaudhuri
authors.forename: Pablo
authors.initials: P
authors.lastname: Martinez-Martin
authors.forename: Pedro
authors.initials: P
authors.lastname: Larrañaga
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology / Q000523:psychology; D011237:Predictive Value of Tests; D011379:Prognosis; D012720:Severity of Illness Index; D012985:Software Design
keywords: Estimation of distribution algorithms; Feature subset selection; Parkinson's disease; Severity indexes