Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis

Irene Azzali, Nicole Dalia Cilia, Claudio De Stefano, Francesco Fontanella, Mario Giacobini, Leonardo Vanneschi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
27 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationApplications of Evolutionary Computation
Subtitle of host publication25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings
EditorsJuan Luis Jiménez LaredoJ, J. Ignacio Hidalgo, Kehinde Oluwatoyin Babaagba
Number of pages14
ISBN (Electronic)978-3-031-02462-7
ISBN (Print)978-3-031-02461-0
Publication statusPublished - 15 Apr 2022
Event25th International Conference on Applications of Evolutionary Computation - Virtual
Duration: 20 Apr 202222 Apr 2022
Conference number: 25

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Applications of Evolutionary Computation
Abbreviated titleEvoApplications 2022
Internet address


  • Alzheimer’s disease
  • Artificial intelligence
  • Handwriting analysis
  • Vectorial genetic programming


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