Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies
dc.contributor.author | Martínez Eguiluz, Maitane | |
dc.contributor.author | Arbelaiz Gallego, Olatz | |
dc.contributor.author | Gurrutxaga Goikoetxea, Ibai | |
dc.contributor.author | Muguerza Rivero, Javier Francisco | |
dc.contributor.author | Perona Balda, Iñigo | |
dc.contributor.author | Murueta-Goyena Larrañaga, Ane | |
dc.contributor.author | Acera Gil, María Ángeles | |
dc.contributor.author | Del Pino Sáez, Rocío | |
dc.contributor.author | Tijero Merino, Beatriz | |
dc.contributor.author | Gómez Esteban, Juan Carlos | |
dc.contributor.author | Gabilondo Cuellar, Iñigo | |
dc.date.accessioned | 2023-03-28T17:27:06Z | |
dc.date.available | 2023-03-28T17:27:06Z | |
dc.date.issued | 2023-03 | |
dc.identifier.citation | Neural Computing and Applications 35(8) : 5603-5617 (2023) | es_ES |
dc.identifier.issn | 1433-3058 | |
dc.identifier.issn | 0941-0643 | |
dc.identifier.uri | http://hdl.handle.net/10810/60537 | |
dc.description.abstract | Non-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening. | es_ES |
dc.description.sponsorship | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially funded by the Department of Education, Universities and Research of the Basque Government (ADIAN, IT-980-16); by the Spanish Ministry of Science, Innovation and Universities - National Research Agency and the European Regional Development Fund - ERDF (PhysComp, TIN2017-85409-P), and from the State Research Agency (AEI, Spain) under grant agreement No RED2018-102312-T (IA-Biomed); by Michael J. Fox Foundation [RRIA 2014 (Rapid Response Innovation Awards) Program (Grant ID: 10189)]; by the Instituto de Salud Carlos III through the project “PI14/00679” and “PI16/00005”, the Juan Rodes grant “JR15/00008” (IG) (Co-funded by European Regional Development Fund/European Social Fund - “Investing in your future”); and by the Department of Health of the Basque Government through the projects “2016111009” and “2019111100”. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Parkinson's disease | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | early detection | es_ES |
dc.subject | non-motor symptoms | es_ES |
dc.title | Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s00521-022-07256-8 | es_ES |
dc.identifier.doi | 10.1007/s00521-022-07256-8 | |
dc.departamentoes | Arquitectura y Tecnología de Computadores | es_ES |
dc.departamentoes | Neurociencias | es_ES |
dc.departamentoeu | Konputagailuen Arkitektura eta Teknologia | es_ES |
dc.departamentoeu | Neurozientziak | es_ES |
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