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. 2021 Jun 21:15:674028.
doi: 10.3389/fncom.2021.674028. eCollection 2021.

Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach

Juan C Laria  1 , David Delgado-Gómez  1   2 , Inmaculada Peñuelas-Calvo  3 , Enrique Baca-García  3   4 , Rosa E Lillo  1   2
Affiliations

Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach

Juan C Laria et al. Front Comput Neurosci. .

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.

Keywords: ADHD; deep learning; feature selection; interpretability; lasso.

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Conflict of interest statement

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.

Figures

Figure 1
Figure 1
Neural network representation of the lasso problem Equation (2).
Figure 2
Figure 2
Evolution of the rmse in the test data over 5,000 iterations for one of the simulations of the first experiment (A) and for one of the second experiment (B).
Figure 3
Figure 3
Weights of the selected items.
See this image and copyright information in PMC

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