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Prediction and Prevalence of Self‐Harm and Nonsuicidal Self‐Injury in Children With Learning Disabilities: A Machine‐Learning Approach in Saudi Arabia.

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Bibliographic Details
Title: Prediction and Prevalence of Self‐Harm and Nonsuicidal Self‐Injury in Children With Learning Disabilities: A Machine‐Learning Approach in Saudi Arabia.
Authors: Almulla, Mazen Omar (AUTHOR), Almulla, Abdullah Ahmed (AUTHOR), Khasawneh, Mohamad Ahmad Saleem (AUTHOR)
Source: Clinical Psychology & Psychotherapy. Mar2026, Vol. 33 Issue 2, p1-17. 17p.
Subjects: Self-injurious behavior, Risk assessment, Cross-sectional method, Self-evaluation, Random forest algorithms, Emotion regulation, Predictive tests, Psychology of children with disabilities, Prediction models, Peer pressure, Research funding, Logistic regression analysis, Socioeconomic status, Questionnaires, Interviewing, Descriptive statistics, Classification of mental disorders, Disease prevalence, Anxiety, Attitudes toward disabilities, Self-mutilation, Support vector machines, Research methodology, Bullying, Machine learning, Comparative studies, Data analysis software, Child psychology, Discrimination against people with disabilities, Learning disabilities, Social classes, Mental depression, Child behavior, Children
Geographic Terms: Saudi Arabia
Abstract: The current study aimed to estimate the prevalence of self‐harm and nonsuicidal self‐injury (NSSI) in children with learning disabilities (LD) in Saudi Arabia and to develop machine‐learning (ML) models to identify individuals at elevated risk. In a cross‐sectional study, 392 children with DSM‐5 specific LD (aged 8–12 years) were recruited through clinical and community channels and assessed for lifetime NSSI and self‐harm using structured interviews and self‐report. A comprehensive set of sociodemographic, academic, clinical and psychosocial variables was screened using recursive feature elimination, and four supervised ML algorithms (penalized logistic regression, random forests, extreme gradient boosting, and support vector machines) and simple ensembles were trained and evaluated using tenfold cross‐validation. Lifetime NSSI was reported by 16.1% of children and self‐harm by 9.2%. All ML models showed excellent discrimination for NSSI (AUC up to 0.99), with extreme gradient boosting and majority‐voting ensembles achieving the best overall performance. For self‐harm, a weighted‐average ensemble yielded the most favourable balance of sensitivity and precision (AUC = 0.93). Across outcomes and algorithms, peer victimization/bullying, emotion dysregulation and depressive symptoms emerged as the most robust predictors, whereas LD severity and anxiety symptoms contributed minimally. Self‐harm and NSSI are common among Saudi children with LD, and ML models can accurately identify those at highest risk, highlighting bullying and emotion dysregulation as key intervention targets in educational and clinical settings. Summary: Self‐harm and nonsuicidal self‐injury (NSSI) appear relatively common among Saudi children with learning disabilities.Simple, interpretable models (penalized logistic regression) performed strongly, while boosting/ensemble approaches offered small additional gains.Peer victimization/bullying consistently emerged as the most robust predictor across outcomes and algorithms.Emotion dysregulation and depressive symptoms also contributed meaningfully, suggesting clear targets for school‐ and clinic‐based prevention.Machine‐learning models should be considered adjunct decision‐support tools and require external validation and calibration before implementation in practice. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
Description
Abstract:The current study aimed to estimate the prevalence of self‐harm and nonsuicidal self‐injury (NSSI) in children with learning disabilities (LD) in Saudi Arabia and to develop machine‐learning (ML) models to identify individuals at elevated risk. In a cross‐sectional study, 392 children with DSM‐5 specific LD (aged 8–12 years) were recruited through clinical and community channels and assessed for lifetime NSSI and self‐harm using structured interviews and self‐report. A comprehensive set of sociodemographic, academic, clinical and psychosocial variables was screened using recursive feature elimination, and four supervised ML algorithms (penalized logistic regression, random forests, extreme gradient boosting, and support vector machines) and simple ensembles were trained and evaluated using tenfold cross‐validation. Lifetime NSSI was reported by 16.1% of children and self‐harm by 9.2%. All ML models showed excellent discrimination for NSSI (AUC up to 0.99), with extreme gradient boosting and majority‐voting ensembles achieving the best overall performance. For self‐harm, a weighted‐average ensemble yielded the most favourable balance of sensitivity and precision (AUC = 0.93). Across outcomes and algorithms, peer victimization/bullying, emotion dysregulation and depressive symptoms emerged as the most robust predictors, whereas LD severity and anxiety symptoms contributed minimally. Self‐harm and NSSI are common among Saudi children with LD, and ML models can accurately identify those at highest risk, highlighting bullying and emotion dysregulation as key intervention targets in educational and clinical settings. Summary: Self‐harm and nonsuicidal self‐injury (NSSI) appear relatively common among Saudi children with learning disabilities.Simple, interpretable models (penalized logistic regression) performed strongly, while boosting/ensemble approaches offered small additional gains.Peer victimization/bullying consistently emerged as the most robust predictor across outcomes and algorithms.Emotion dysregulation and depressive symptoms also contributed meaningfully, suggesting clear targets for school‐ and clinic‐based prevention.Machine‐learning models should be considered adjunct decision‐support tools and require external validation and calibration before implementation in practice. [ABSTRACT FROM AUTHOR]
ISSN:10633995
DOI:10.1002/cpp.70245