Predicting autistic traits, anxiety and depression symptoms using camouflaging autistic traits questionnaire (CAT-Q-ES): A machine learning study / Sabela CONDE-PUMPIDO ZUBIZARRETA in Research in Autism, 128 (October 2025)  

Public ISBD [article]  inResearch in Autism > 128 (October 2025) . - p.202712 Titre : Predicting autistic traits, anxiety and depression symptoms using camouflaging autistic traits questionnaire (CAT-Q-ES): A machine learning study Type de document : texte imprimé Auteurs : Sabela CONDE-PUMPIDO ZUBIZARRETA, Auteur ; María TUBÍO-FUNGUEIRIÑO, Auteur ; Marta POZO-RODRÍGUEZ, Auteur ; Ángel CARRACEDO, Auteur ; Eva CERNADAS, Auteur ; Manuel FERNÁNDEZ-DELGADO, Auteur ; Montse FERNÁNDEZ-PRIETO, Auteur Article en page(s) : p.202712 Langues : Anglais (eng) Mots-clés : ASD  Autism  CAT-Q-ES  Camouflaging  Supervised machine learning  Mental health  Gender Index. décimale : PER Périodiques Résumé : Research has linked camouflaging with compensating and hiding autistic traits during social interactions. Furthermore, these strategies have been linked to increased anxiety and depression symptoms and to greater reliance on camouflaging behaviors among individuals with more autistic traits, even in non-autistic populations. This study evaluated the viability of a machine learning algorithm to predict autistic traits and symptoms of depression and anxiety using camouflaging behaviors. The sample included 601 participants: 102 autistic adults (72 women, 18 men, and 12 non-binary individuals) and 499 non-autistic adults (399 women, 92 men, and eight non-binary individuals). The study predicted autistic traits measured with the Broader Autism Phenotype Questionnaire (BAPQ) subscales - Aloofness, Pragmatics, and Rigidity - as well as the total score of depressive (Patient Health Questionnaire - PHQ-9) and anxious symptoms (General Anxiety Disorder - GAD-7) using the individual items from the Camouflaging Autistic Traits Questionnaire Spanish version (CAT-Q-ES) as predictors. We developed fifty supervised learning models, including support vector machines, neural networks, linear regressors, decision trees, random forests, and Gaussian processes, among others. Correlation coefficients between true and predicted scores were strong for Aloofness (R=.85), Pragmatics (R=.82), and Rigidity (R=.74), being only moderate for Depression (R=.60) and Anxiety (R=.54). Autism diagnosis or gender identity did not improve the prediction’s accuracy. These results show the viability of machine learning algorithms to predict autistic traits (Aloofness, Pragmatics and Rigidity) and anxiety-depression symptoms, using the CAT-Q-ES. This suggests potential for developing a tool that may improve autistic traits and emotional problems screening in individuals whose diagnosis is unclear or not yet established, regardless of gender identity. En ligne : https://doi.org/10.1016/j.reia.2025.202712 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=570 [article] Predicting autistic traits, anxiety and depression symptoms using camouflaging autistic traits questionnaire (CAT-Q-ES): A machine learning study [texte imprimé] / Sabela CONDE-PUMPIDO ZUBIZARRETA, Auteur ; María TUBÍO-FUNGUEIRIÑO, Auteur ; Marta POZO-RODRÍGUEZ, Auteur ; Ángel CARRACEDO, Auteur ; Eva CERNADAS, Auteur ; Manuel FERNÁNDEZ-DELGADO, Auteur ; Montse FERNÁNDEZ-PRIETO, Auteur . - p.202712. Langues : Anglais (eng) in Research in Autism > 128 (October 2025) . - p.202712 Mots-clés : ASD  Autism  CAT-Q-ES  Camouflaging  Supervised machine learning  Mental health  Gender Index. décimale : PER Périodiques Résumé : Research has linked camouflaging with compensating and hiding autistic traits during social interactions. Furthermore, these strategies have been linked to increased anxiety and depression symptoms and to greater reliance on camouflaging behaviors among individuals with more autistic traits, even in non-autistic populations. This study evaluated the viability of a machine learning algorithm to predict autistic traits and symptoms of depression and anxiety using camouflaging behaviors. The sample included 601 participants: 102 autistic adults (72 women, 18 men, and 12 non-binary individuals) and 499 non-autistic adults (399 women, 92 men, and eight non-binary individuals). The study predicted autistic traits measured with the Broader Autism Phenotype Questionnaire (BAPQ) subscales - Aloofness, Pragmatics, and Rigidity - as well as the total score of depressive (Patient Health Questionnaire - PHQ-9) and anxious symptoms (General Anxiety Disorder - GAD-7) using the individual items from the Camouflaging Autistic Traits Questionnaire Spanish version (CAT-Q-ES) as predictors. We developed fifty supervised learning models, including support vector machines, neural networks, linear regressors, decision trees, random forests, and Gaussian processes, among others. Correlation coefficients between true and predicted scores were strong for Aloofness (R=.85), Pragmatics (R=.82), and Rigidity (R=.74), being only moderate for Depression (R=.60) and Anxiety (R=.54). Autism diagnosis or gender identity did not improve the prediction’s accuracy. These results show the viability of machine learning algorithms to predict autistic traits (Aloofness, Pragmatics and Rigidity) and anxiety-depression symptoms, using the CAT-Q-ES. This suggests potential for developing a tool that may improve autistic traits and emotional problems screening in individuals whose diagnosis is unclear or not yet established, regardless of gender identity. En ligne : https://doi.org/10.1016/j.reia.2025.202712 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=570

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