Abstract
The problem of lack of data remains a major drawback regardless of the continuous evolution of machine learning and deep learning models. This paper presents a modular and scalable architecture for the prediction and treatment of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). With this architecture, therapists will be able to collect data from individuals from anywhere and at anytime, thanks to mobile devices, which will enable personalised monitoring. One of the main objectives of this ongoing project, which has a very widespread international projection within the framework of social inclusion, is the creation of a new collection of data due to the lack of data in this area. As a result, we will be able to place it in important repositories and specialised journals and thereby make it available to the scientific community. This architecture has been evaluated with several supervised and unsupervised machine learning algorithms in order to identify possible candidates for ASD/ADHD diagnosis. The initial results are very encouraging despite the small volume of data because they allow the possibility of developing personalised dashboards, which specialists can adapt to the personalised treatment of each individual according to the indicators obtained.
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Notes
- 1.
https://archive.ics.uci.edu/ml/index.php (visited on 1 July 2022).
- 2.
https://www.kaggle.com/ (visited on 1 July 2022).
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Acknowledgment
This research has been partially funded by the BALLADEER project (PROMETEO/2021/088) from the Consellería de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana and by the AETHER-UA (PID2020-112540RB-C43) project from the Spanish Ministry of Science and Innovation. This work has been also partially funded by “La Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital”, under the project “Development of an architecture based on machine learning and data mining techniques for the prediction of indicators in the diagnosis and intervention of autism spectrum disorder. AICO/2020/117”.
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del Mar Guillén, M., Amador, S., Peral, J., Gil, D., Elouali, A. (2023). Overcoming the Lack of Data to Improve Prediction and Treatment of Individuals with Autistic Spectrum Disorder and Attention Deficit Hyperactivity Disorder. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_75
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