- author = "Irene Azzali and Nicole Dalia Cilia and Claudio {De Stefano} and Francesco Fontanella and Mario Giacobini and Leonardo Vanneschi",
- title = "Vectorial {GP for Alzheimer's} Disease Prediction Through Handwriting Analysis",
- booktitle = "25th International Conference, EvoApplications 2022",
- year = "2022",
- month = "20-22 " # apr,
- editor = "Juan Luis Jimenez Laredo and J. Ignacio Hidalgo and Kehinde Oluwatoyin Babaagba",
- series = "LNCS",
- volume = "13224",
- publisher = "Springer",
- address = "Madrid",
- pages = "517--530",
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming, Alzheimer disease, Artificial intelligence, Handwriting analysis, Vectorial genetic programming",
- isbn13 = "978-3-031-02461-0",
-
DOI = "
doi:10.1007/978-3-031-02462-7_33",
- abstract = "Alzheimer 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.",
-
notes = "Also (?) presented at WIVACE 2022 XVI International
Workshop on Artificial Life and Evolutionary
Computation Gaeta (LT), Italy September 14-16, 2022
http://wivace2022.unicas.it/files/programWIVACE2022.pdf
http://www.evostar.org/2022/ EvoApplications2022 held in conjunction with EuroGP'2022, EvoCOP2022 and EvoMusArt2022",
