Predicting Suicidal Ideation Among Youths With Autism Spectrum Disorder: An Advanced Machine Learning Study.
This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by applying machine learning techniques. A cross‐sectional sample of 368 ASD‐diagnosed young people (aged 18–24 years) was recruited, and 34 candidate predictors—including sociodemographic characteristics,...
| Published in: | Clinical Psychology & Psychotherapy Vol. 32; no. 3; pp. 1 - 14 |
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| Main Authors: | , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
May/Jun2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186163168&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186163168 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10633995 BUX jtl: Clinical Psychology & Psychotherapy issn: 10633995 maglogo: Y pubinfo: dt: May/Jun2025 vid: 32 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 186163168 186163168 186163168 10.1002/cpp.70082 186163168 ppf: 1 ppct: 13 formats: tig: atl: Predicting Suicidal Ideation Among Youths With Autism Spectrum Disorder: An Advanced Machine Learning Study. aug: au: Al‐Srehan, Hussein Ayasrah, Mohammad Nayef Al‐Rousan, Ayoub Hamdan Khasawneh, Mohamad Ahmad Saleem Gharaibeh, Mahmoud affil: College of Education, Humanities and Social Sciences, Al Ain University, Abu Dhabi, United Arab Emirates sug: subj: Autism Spectrum Disorder Complications Suicidal Ideation Risk Factors Risk Assessment Machine Learning Utilization Prediction Models Human Sociodemographic Factors Anxiety Symptoms Depression Symptoms Bullying Insomnia Complications Adverse Childhood Experiences Male Female Adolescence Young Adult Descriptive Statistics Data Analysis Software Cross Sectional Studies Algorithms Scales Questionnaires Logistic Regression Adolescent: 13-18 years Male Female ab: This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by applying machine learning techniques. A cross‐sectional sample of 368 ASD‐diagnosed young people (aged 18–24 years) was recruited, and 34 candidate predictors—including sociodemographic characteristics, psychiatric symptoms (e.g., anxiety problems and depressive symptoms), behavioural measures (e.g., bullying victimization and insomnia severity) and adverse childhood experiences—were assessed using standardized instruments and parent‐report checklists. After listwise deletion of missing data, recursive feature elimination (RFE) with a random forest wrapper was performed to identify the five most influential predictors. Four classification algorithms (logistic regression, random forest, eXtreme Gradient Boosting [XGBoost] and support vector machine [SVM]) were then trained on a 70/30 stratified split and evaluated on the hold‐out test set using area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and accuracy. RFE identified anxiety problems, insomnia, bullying victimization, age and depression (PHQ‐9) as the top predictors. Logistic regression achieved an AUC of 0.943 (sensitivity = 0.773, specificity = 0.957 and accuracy = 0.922), random forest an AUC of 0.948 (sensitivity = 0.727, specificity = 0.989 and accuracy = 0.939), XGBoost an AUC of 0.930 (sensitivity = 0.772, specificity = 0.989 and accuracy = 0.947) and SVM an AUC of 0.942 (sensitivity = 0.772, specificity = 0.978 and accuracy = 0.939). Across models, anxiety and insomnia emerged as the two most important risk factors, and XGBoost demonstrated the best overall balance of performance metrics, yielding the highest accuracy. Gradient‐boosted tree models were thus shown to effectively integrate multidimensional data to predict suicidality in autistic youth, highlighting anxiety and sleep disturbances as critical targets for personalized risk assessment and prevention efforts. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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