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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.
Title: | Unveiling relevant non-motor Parkinson’s disease severity symptoms using a machine learning approach |
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Author/s: |
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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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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.
Item ID: | 72859 |
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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 |