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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asan, O., Bayrak, A.E., Choudhury, A.: Artificial intelligence and human trust in healthcare: focus on clinicians. J. Med. Internet Res. 22(6) (2020). https://doi.org/10.2196/15154
Azzali, I., Vanneschi, L., Bakurov, I., Silva, S., Ivaldi, M., Giacobini, M.: Towards the use of vector based gp to predict physiological time series. Appl. Soft Comput., 89 (2020). https://doi.org/10.1016/j.asoc.2020.106097
Azzali, I., Vanneschi, L., Mosca, A., Bertolotti, L., Giacobini, M.: Towards the use of genetic programming in the ecological modelling of mosquito population dynamics. Genet. Program Evolvable Mach. 21(4), 629–642 (2020). https://doi.org/10.1007/s10710-019-09374-0
Azzali, I., Vanneschi, L., Silva, S., Bakurov, I., Giacobini, M.: Review of classification using genetic programming. In: Genetic Programming, EuroGP 2019, Lecture Notes in Computer Science (2019)
Bakurov, I., Castelli, M., Vanneschi, L., Freitas, M.J.: Supporting medical decisions for treating rare diseases through genetic programming. In: Kaufmann, P., Castillo, P.A. (eds.) EvoApplications 2019. LNCS, vol. 11454, pp. 187–203. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16692-2_13
Bojarczuk, C.C., Lopes, H.S., Freitas, A.A., Michalkiewicz, E.L.: A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets. Artif. Intell. Med. 30(1), 27–48 (2004)
Castelli, M., Vanneschi, L., Manzoni, L., Popovič, A.: Semantic genetic programming for fast and accurate data knowledge discovery. Swarm Evol. Comput. 26, 1–7 (2016)
Cilia, N.D., De Stefano, C., Fontanella, F., Molinara, M., Scotto Di Freca, A.: Handwriting analysis to support alzheimer’s disease diagnosis: a preliminary study. In: Vento, M., Percannella, G. (eds.) CAIP 2019. LNCS, vol. 11679, pp. 143–151. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29891-3_13
Cilia, N., De Stefano, C., Fontanella, F., Scotto Di Freca, A.: An experimental protocol to support cognitive impairment diagnosis by using handwriting analysis. In: Procedia Computer Science, Proceeding of The 8th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH), pp. 1–9. Elsevier (2019)
Cilia, N.D., De Stefano, C., Fontanella, F., Di Freca, A.S.: Using genetic algorithms for the prediction of cognitive impairments. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds.) EvoApplications 2020. LNCS, vol. 12104, pp. 479–493. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43722-0_31
De Falco, I., Tarantino, E., Cioppa, A., Fontanella, F.: An innovative approach to genetic programming-based clustering. Adv. Soft Comput. 34, 55–64 (2006)
De Falco, I., Tarantino, E., Della Cioppa, A., Fontanella, F.: A novel grammer-based genetic programming approach to clustering. In: Proceedings of the ACM Symposium on Applied Computing, vol. 2, pp. 928–932 (2005)
Diaz, M., Ferrer, M.A., Impedovo, D., Pirlo, G., Vessio, G.: Dynamically enhanced static handwriting representation for parkinson’s disease detection. Pattern Recogn. Lett. 128(204–210) (2019)
Garre-Olmo, J., Faundez-Zanuy, M., de Ipiña, K.L., Calvo-Perxas, L., Turro-Garriga, O.: Kinematic and pressure features of handwriting and drawing: Preliminary results between patients with mild cognitive impairment, alzheimer disease and healthy controls. Curr. Alzheimer Res. 14, 1–9 (2017)
Ghaheri, A., Shoar, S., Naderan, M., Hoseini, S.S.: The applications of genetic algorithms in medicine. Oman Med. J. 30(6), 406–416 (2015)
Impedovo, D., Pirlo, G.: Dynamic handwriting analysis for the assessment of neurodegenerative diseases: a pattern recognition perspective. IEEE Reviews in Biomedical Engineering, pp. 1–13 (2018)
Jabeen, H., Baig, A.: Review of classification using genetic programming. In: International Journal of Engineering Science and Technology (2010)
Johnson, P., et al.: Genetic algorithm with logistic regression for prediction of progression to alzheimer’s disease. BMC Bioinform. 15(S11) (2014)
Koza, J.R.: Genetic programming: On the programming of computers by means of natural selection. In: MIT Press, Cambridge (1992)
Onofri, E., Mercuri, M., Archer, T., Ricciardi, M.R., F.Massoni, Ricci, S.: Effect of cognitive fluctuation on handwriting in alzheimer’s patient: a case study. Acta Medica Mediterranea 3, 751 (2015)
Onofri, E., Mercuri, M., Salesi, M., Ricciardi, M., Archer, T.: Dysgraphia in relation to cognitive performance in patients with Alzheimer’s disease. J. Intellectual Disability-Diagnosis Treatment 1, 113–124 (2013)
Parziale, A., Senatore, R., Della Cioppa, A., Marcelli, A.: Cartesian genetic programming for diagnosis of parkinson disease through handwriting analysis: performance vs. interpretability issues. Artif. Intell. Med. 111, 101984 (2021)
Petrowski, A., Ben-Hamida, S.: Evolutionary algorithms. In: Wiley-ISTE (2020)
Poli, R., Langdon, W., McPhee, N.: A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (2008)
Quinlan, J.R.: C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning). Morgan Kaufmann, San Francisco (1993)
Valenzuela, O., Jiang, X., Carrillo, A., Rojas, I.: Multi-objective genetic algorithms to find most relevant volumes of the brain related to alzheimer’s disease and mild cognitive impairment. Int. J. Neural Syst. 28(09) (2018)
Vanneschi, L., Castelli, M.: Soft target and functional complexity reduction: A hybrid regularization method for genetic programming. Expert Syst. Appl. 177, 114929 (2021)
Werner, P., Rosenblum, S., Bar-On, G., Heinik, J., Korczyn, A.: Handwriting process variables discriminating mild alzheimer’s disease and mild cognitive impairment. J. Gerontology: PSYCHOLOGICAL SCIENCES 61(4), 228–36 (2006)
Acknowledgments
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
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: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-031-02462-7_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-02461-0
Online ISBN: 978-3-031-02462-7
eBook Packages: Computer ScienceComputer Science (R0)