Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach
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 particul...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2021.674028/full |
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| author | Juan C. Laria David Delgado-Gómez David Delgado-Gómez Inmaculada Peñuelas-Calvo Enrique Baca-García Enrique Baca-García Rosa E. Lillo Rosa E. Lillo |
| author_facet | Juan C. Laria David Delgado-Gómez David Delgado-Gómez Inmaculada Peñuelas-Calvo Enrique Baca-García Enrique Baca-García Rosa E. Lillo Rosa E. Lillo |
| author_sort | Juan C. Laria |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T10:03:23Z |
| format | Article |
| id | doaj.art-3e8cbe07b29c4663952975873beb3cdc |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-21T10:03:23Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-3e8cbe07b29c4663952975873beb3cdc2022-12-21T19:07:53ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882021-06-011510.3389/fncom.2021.674028674028Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso ApproachJuan C. Laria0David Delgado-Gómez1David Delgado-Gómez2Inmaculada Peñuelas-Calvo3Enrique Baca-García4Enrique Baca-García5Rosa E. Lillo6Rosa E. Lillo7Department of Statistics, University Carlos III of Madrid, Madrid, SpainDepartment of Statistics, University Carlos III of Madrid, Madrid, SpainSantander Big Data Institute, Universidad Carlos III de Madrid, Madrid, SpainDepartment of Psychiatry, Fundación Jiménez Díaz Hospital, Madrid, SpainDepartment of Psychiatry, Fundación Jiménez Díaz Hospital, Madrid, SpainDepartment of Psychiatry, Nimes University Hospital, Nimes, FranceDepartment of Statistics, University Carlos III of Madrid, Madrid, SpainSantander Big Data Institute, Universidad Carlos III de Madrid, Madrid, SpainThe 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.https://www.frontiersin.org/articles/10.3389/fncom.2021.674028/fulldeep learninglassofeature selectioninterpretabilityADHD |
| spellingShingle | Juan C. Laria David Delgado-Gómez David Delgado-Gómez Inmaculada Peñuelas-Calvo Enrique Baca-García Enrique Baca-García Rosa E. Lillo Rosa E. Lillo Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach Frontiers in Computational Neuroscience deep learning lasso feature selection interpretability ADHD |
| title | Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach |
| title_full | Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach |
| title_fullStr | Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach |
| title_full_unstemmed | Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach |
| title_short | Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach |
| title_sort | accurate prediction of children s adhd severity using family burden information a neural lasso approach |
| topic | deep learning lasso feature selection interpretability ADHD |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2021.674028/full |
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