Vectorial GP for Alzheimer's Disease Prediction Through Handwriting Analysis
Created by W.Langdon from
gp-bibliography.bib Revision:1.6717
- @InProceedings{Azzali:2022:evoapplications,
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author = "Irene Azzali and Nicole Dalia Cilia and
Claudio {De Stefano} and Francesco Fontanella and
Mario Giacobini and Leonardo Vanneschi",
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title = "Vectorial {GP for Alzheimer's} Disease Prediction
Through Handwriting Analysis",
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booktitle = "25th International Conference, EvoApplications 2022",
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year = "2022",
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month = "20-22 " # apr,
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editor = "Juan Luis Jimenez Laredo and J. Ignacio Hidalgo and
Kehinde Oluwatoyin Babaagba",
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series = "LNCS",
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volume = "13224",
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publisher = "Springer",
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address = "Madrid",
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pages = "517--530",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Alzheimer
disease, Artificial intelligence, Handwriting analysis,
Vectorial genetic programming",
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isbn13 = "978-3-031-02461-0",
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DOI = "
doi:10.1007/978-3-031-02462-7_33",
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abstract = "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.",
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notes = "http://www.evostar.org/2022/ EvoApplications2022 held
in conjunction with EuroGP'2022, EvoCOP2022 and
EvoMusArt2022",
- }
Genetic Programming entries for
Irene Azzali
Nicole Dalia Cilia
Claudio De Stefano
Francesco R Fontanella
Mario Giacobini
Leonardo Vanneschi
Citations