Machine Learning-Based Early Prediction Model for Autism Spectrum Disorder in Infants Using Acoustic Feature / Shengjian YIN in Autism Research, 19-3 (March 2026)  

Public ISBD [article]  inAutism Research > 19-3 (March 2026) . - e70179 Titre : Machine Learning-Based Early Prediction Model for Autism Spectrum Disorder in Infants Using Acoustic Feature Type de document : texte imprimé Auteurs : Shengjian YIN, Auteur ; Zhijia LI, Auteur ; Luyang GUAN, Auteur ; Zenghe YUE, Auteur ; Jincen WANG, Auteur ; Jinyi ZHU, Auteur ; Yazhu HAN, Auteur ; Qian LI, Auteur ; Lan LIN, Auteur ; Yaxin DAI, Auteur ; Haozhen CHEN, Auteur ; Yuheng CHEN, Auteur ; Yun LI, Auteur ; Xiaoyan KE, Auteur Article en page(s) : e70179 Langues : Anglais (eng) Mots-clés : acoustic features  autism spectrum disorder  machine learning  model  support vector machine Index. décimale : PER Périodiques Résumé : ABSTRACT This study aimed to create a machine learning-based predictive model for early detection of autism spectrum disorder (ASD) in infants using acoustic features. Conducted as a prospective cohort at Nanjing Medical University from 2019 to 2024, infants aged 9?18?months from an ASD sibling cohort participated. Behavioral and vocalization data were gathered during the Still-Face Paradigm, with ASD diagnoses confirmed at 36?months through ADOS and ADI-R assessments. Researchers extracted 4368 acoustic features from the recordings and applied LASSO regression for dimensionality reduction, identifying 39 key features. A support vector machine (SVM) classifier was then developed, tested with four kernel functions?linear, radial basis function, polynomial, and sigmoid?via tenfold cross-validation. The final sample included 88 infants, 28 of whom were diagnosed with ASD. The sigmoid kernel yielded the best results, achieving a 92.86% sensitivity, 93.33% specificity, and a 93.18% accuracy. Notably, spectral and energy-related features were significantly higher in ASD infants (p? En ligne : https://doi.org/10.1002/aur.70179 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=583 [article] Machine Learning-Based Early Prediction Model for Autism Spectrum Disorder in Infants Using Acoustic Feature [texte imprimé] / Shengjian YIN, Auteur ; Zhijia LI, Auteur ; Luyang GUAN, Auteur ; Zenghe YUE, Auteur ; Jincen WANG, Auteur ; Jinyi ZHU, Auteur ; Yazhu HAN, Auteur ; Qian LI, Auteur ; Lan LIN, Auteur ; Yaxin DAI, Auteur ; Haozhen CHEN, Auteur ; Yuheng CHEN, Auteur ; Yun LI, Auteur ; Xiaoyan KE, Auteur . - e70179. Langues : Anglais (eng) in Autism Research > 19-3 (March 2026) . - e70179 Mots-clés : acoustic features  autism spectrum disorder  machine learning  model  support vector machine Index. décimale : PER Périodiques Résumé : ABSTRACT This study aimed to create a machine learning-based predictive model for early detection of autism spectrum disorder (ASD) in infants using acoustic features. Conducted as a prospective cohort at Nanjing Medical University from 2019 to 2024, infants aged 9?18?months from an ASD sibling cohort participated. Behavioral and vocalization data were gathered during the Still-Face Paradigm, with ASD diagnoses confirmed at 36?months through ADOS and ADI-R assessments. Researchers extracted 4368 acoustic features from the recordings and applied LASSO regression for dimensionality reduction, identifying 39 key features. A support vector machine (SVM) classifier was then developed, tested with four kernel functions?linear, radial basis function, polynomial, and sigmoid?via tenfold cross-validation. The final sample included 88 infants, 28 of whom were diagnosed with ASD. The sigmoid kernel yielded the best results, achieving a 92.86% sensitivity, 93.33% specificity, and a 93.18% accuracy. Notably, spectral and energy-related features were significantly higher in ASD infants (p? En ligne : https://doi.org/10.1002/aur.70179 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=583

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