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Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis
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Resumo(s)
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.
Descrição
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).
Palavras-chave
Alzheimer’s disease Artificial intelligence Handwriting analysis Vectorial genetic programming Theoretical Computer Science General Computer Science SDG 3 - Good Health and Well-being
Contexto Educativo
Citação
Editora
Springer