Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis

Detalhes bibliográficos
Autor(a) principal: Azzali, Irene
Data de Publicação: 2022
Outros Autores: Cilia, Nicole Dalia, De Stefano, Claudio, Fontanella, Francesco, Giacobini, Mario, Vanneschi, Leonardo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/142306
Resumo: Azzali, I., Cilia, N. D., De Stefano, C., Fontanella, F., Giacobini, M., & Vanneschi, L. (2022). Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis. In J. L. Jiménez LaredoJ, J. I. Hidalgo, & K. O. Babaagba (Eds.), Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (pp. 517-530). (Lecture Notes in Computer Science; Vol. 13224). Springer. https://doi.org/10.1007/978-3-031-02462-7_33 ----------------------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 also supported by MIUR (Minister for Education, University and Research, Law 232/216, Department of Excellence).
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spelling Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting AnalysisAlzheimer’s diseaseArtificial intelligenceHandwriting analysisVectorial genetic programmingTheoretical Computer ScienceComputer Science(all)SDG 3 - Good Health and Well-beingAzzali, I., Cilia, N. D., De Stefano, C., Fontanella, F., Giacobini, M., & Vanneschi, L. (2022). Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis. In J. L. Jiménez LaredoJ, J. I. Hidalgo, & K. O. Babaagba (Eds.), Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (pp. 517-530). (Lecture Notes in Computer Science; Vol. 13224). Springer. https://doi.org/10.1007/978-3-031-02462-7_33 ----------------------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 also supported by MIUR (Minister for Education, University and Research, Law 232/216, Department of Excellence).Alzheimer’s Disease (AD) is a neurodegenerative disease which causes a continuous cognitive decline. This decline has a strong impact on daily life of the people affected and on that of their relatives. Unfortunately, to date there is no cure for this disease. However, its early diagnosis helps to better manage the course of the disease with the treatments currently available. In recent years, AI researchers have become increasingly interested in developing tools for early diagnosis of AD based on handwriting analysis. In most cases, they use a feature engineering approach: domain knowledge by clinicians is used to define the set of features to extract from the raw data. 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 a recently defined method that enhances Genetic Programming (GP) and is able to directly manage time series in such a way to automatically extract informative features, without any need of human intervention. We applied VE_GP to handwriting data in the form of time series consisting of spatial coordinates and pressure. These time series represent pen movements collected from people while performing handwriting tasks. The presented experimental results indicate that the proposed approach is effective for this type of application. Furthermore, VE_GP is also able to generate rather small and simple models, that can be read and possibly interpreted. These models are reported and discussed in the Last part of the paper.SpringerNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAzzali, IreneCilia, Nicole DaliaDe Stefano, ClaudioFontanella, FrancescoGiacobini, MarioVanneschi, Leonardo2024-01-25T01:31:59Z2022-04-152022-04-15T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion14application/pdfhttp://hdl.handle.net/10362/142306eng978-3-031-02461-00302-9743PURE: 43490864https://doi.org/10.1007/978-3-031-02462-7_33info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:03:48Zoai:run.unl.pt:10362/142306Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:34:28.205273Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis
title Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis
spellingShingle Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis
Azzali, Irene
Alzheimer’s disease
Artificial intelligence
Handwriting analysis
Vectorial genetic programming
Theoretical Computer Science
Computer Science(all)
SDG 3 - Good Health and Well-being
title_short Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis
title_full Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis
title_fullStr Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis
title_full_unstemmed Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis
title_sort Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis
author Azzali, Irene
author_facet Azzali, Irene
Cilia, Nicole Dalia
De Stefano, Claudio
Fontanella, Francesco
Giacobini, Mario
Vanneschi, Leonardo
author_role author
author2 Cilia, Nicole Dalia
De Stefano, Claudio
Fontanella, Francesco
Giacobini, Mario
Vanneschi, Leonardo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Azzali, Irene
Cilia, Nicole Dalia
De Stefano, Claudio
Fontanella, Francesco
Giacobini, Mario
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Alzheimer’s disease
Artificial intelligence
Handwriting analysis
Vectorial genetic programming
Theoretical Computer Science
Computer Science(all)
SDG 3 - Good Health and Well-being
topic Alzheimer’s disease
Artificial intelligence
Handwriting analysis
Vectorial genetic programming
Theoretical Computer Science
Computer Science(all)
SDG 3 - Good Health and Well-being
description Azzali, I., Cilia, N. D., De Stefano, C., Fontanella, F., Giacobini, M., & Vanneschi, L. (2022). Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis. In J. L. Jiménez LaredoJ, J. I. Hidalgo, & K. O. Babaagba (Eds.), Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (pp. 517-530). (Lecture Notes in Computer Science; Vol. 13224). Springer. https://doi.org/10.1007/978-3-031-02462-7_33 ----------------------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 also supported by MIUR (Minister for Education, University and Research, Law 232/216, Department of Excellence).
publishDate 2022
dc.date.none.fl_str_mv 2022-04-15
2022-04-15T00:00:00Z
2024-01-25T01:31:59Z
dc.type.driver.fl_str_mv conference object
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/142306
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dc.language.iso.fl_str_mv eng
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PURE: 43490864
https://doi.org/10.1007/978-3-031-02462-7_33
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dc.publisher.none.fl_str_mv Springer
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dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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