Publication: Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach
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2021-06-21
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Frontiers Media
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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.
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Adhd, Deep learning, Feature selection, Interpretability, Lasso
Bibliographic citation
Laria, J. C., Delgado-Gómez, D., Peñuelas-Calvo, I., Baca-García, E., & Lillo, R. E. (2021). Accurate Prediction of Children’s ADHD Severity Using Family Burden Information: A Neural Lasso Approach. Frontiers in Computational Neuroscience, 15.