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Automatic feature extraction with Vectorial Genetic Programming for Alzheimer’s Disease prediction through handwriting analysis

2024-06Artigo científico Acesso aberto
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dc.contributor.authorAzzali, Irene
dc.contributor.authorCilia, Nicole D.
dc.contributor.authorDe Stefano, Claudio
dc.contributor.authorFontanella, Francesco
dc.contributor.authorGiacobini, Mario
dc.contributor.authorVanneschi, Leonardo
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblElsevier
dc.date.accessioned2024-04-16T00:30:17Z
dc.date.embargoedUntil2026-04-10
dc.date.issued2024-06
dc.descriptionAzzali, I., Cilia, N. D., De Stefano, C., Fontanella, F., Giacobini, M., & Vanneschi, L. (2024). Automatic feature extraction with Vectorial Genetic Programming for Alzheimer’s Disease prediction through handwriting analysis. Swarm and Evolutionary Computation, 87, 1-11. Article 101571. https://doi.org/10.1016/j.swevo.2024.101571 --- This work was partially supported by FCT, Portugal, through funding of projects BINDER (PTDC/CCI-INF/29168/2017) and AICE (DSAIPA/DS/0113/2019). This work was partially supported by EU in NextGenerationEU plan through MUR Decree n. 1051 23.06.2022 PNRR Missione 4 Componente 2 Investimento 1.5 - CUP H33C22000420001.
dc.description.abstractAlzheimer’s Disease (AD) is an incurable neurodegenerative disease that strongly impacts the lives of the people affected. Even if, to date, there is no cure for this disease, its early diagnosis helps to manage the course of the disease better with the treatments currently available. Even more importantly, an early diagnosis will also be necessary for the new treatments available in the future. Recently, machine learning (ML) based tools have demonstrated their effectiveness in recognizing people’s handwriting in the early stages of AD. In most cases, they use features defined by using the domain knowledge provided by clinicians. In this paper, we present a novel approach based on vectorial genetic programming (VE_GP) to recognize the handwriting of AD patients. VE_GP is an enhanced version of GP that can manage time series directly. We applied VE_GP to data collected using an experimental protocol, which was defined to collect handwriting data to support the development of ML tools for the early diagnosis of AD based on handwriting analysis. The experimental results confirmed the effectiveness of the proposed approach in terms of classification performance, size, and simplicity.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent11
dc.format.extent442977
dc.identifier.doi10.1016/j.swevo.2024.101571
dc.identifier.issn2210-6502
dc.identifier.otherPURE: 88226161
dc.identifier.otherPURE UUID: 59ec6841-ae76-4c23-93b0-2a5885a23228
dc.identifier.othercrossref: 10.1016/j.swevo.2024.101571
dc.identifier.otherScopus: 85190160413
dc.identifier.otherWOS: 001226555800001
dc.identifier.otherORCID: /0000-0003-4732-3328/work/157603007
dc.identifier.urihttp://hdl.handle.net/10362/166254
dc.identifier.urlhttps://doi.org/10.24432/C55D0K
dc.identifier.urlhttps://www.scopus.com/pages/publications/85190160413
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001226555800001
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0113%2F2019/PT
dc.relationData Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal
dc.subjectVectorial Genetic Programming
dc.subjectAlzheimer’s Disease
dc.subjectMachine learning
dc.subjectHealthcare applications
dc.subjectGeneral Computer Science
dc.subjectGeneral Mathematics
dc.subjectSDG 3 - Good Health and Well-being
dc.titleAutomatic feature extraction with Vectorial Genetic Programming for Alzheimer’s Disease prediction through handwriting analysisen
dc.typejournal article
degois.publication.firstPage1
degois.publication.issueJune
degois.publication.lastPage11
degois.publication.titleSwarm and Evolutionary Computation
degois.publication.volume87
dspace.entity.typePublication
oaire.awardNumberDSAIPA/DS/0113/2019
oaire.awardNumberPTDC/CCI-INF/29168/2017
oaire.awardTitleData Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0113%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublicatione750f897-cfb5-46b7-84ff-3b768eeb44f6
relation.isProjectOfPublication2624e8d1-5a03-474c-b4a2-34987301953a
relation.isProjectOfPublication.latestForDiscoverye750f897-cfb5-46b7-84ff-3b768eeb44f6

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