Back to Search
Start Over
Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach.
- Source :
-
Frontiers in computational neuroscience [Front Comput Neurosci] 2021 Jun 21; Vol. 15, pp. 674028. Date of Electronic Publication: 2021 Jun 21 (Print Publication: 2021). - Publication Year :
- 2021
-
Abstract
- The deep lasso algorithm (dlasso) is introduced as a neural version of the statistical linear lasso algorithm that holds benefits from both methodologies: feature selection and automatic optimization of the parameters (including the regularization parameter). This last property makes dlasso particularly attractive for feature selection on small samples. In the two first conducted experiments, it was observed that dlasso is capable of obtaining better performance than its non-neuronal version (traditional lasso), in terms of predictive error and correct variable selection. Once that dlasso performance has been assessed, it is used to determine whether it is possible to predict the severity of symptoms in children with ADHD from four scales that measure family burden, family functioning, parental satisfaction, and parental mental health. Results show that dlasso is able to predict parents' assessment of the severity of their children's inattention from only seven items from the previous scales. These items are related to parents' satisfaction and degree of parental burden.<br />Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2021 Laria, Delgado-Gómez, Peñuelas-Calvo, Baca-García and Lillo.)
Details
- Language :
- English
- ISSN :
- 1662-5188
- Volume :
- 15
- Database :
- MEDLINE
- Journal :
- Frontiers in computational neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 34234664
- Full Text :
- https://doi.org/10.3389/fncom.2021.674028