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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| Title: | 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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| 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] |
| Copyright of Clinical Psychology & Psychotherapy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 193280234 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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Mar2026, Vol. 33 Issue 2, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Self-injurious+behavior%22">Self-injurious behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Cross-sectional+method%22">Cross-sectional method</searchLink><br /><searchLink fieldCode="DE" term="%22Self-evaluation%22">Self-evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Emotion+regulation%22">Emotion regulation</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+tests%22">Predictive tests</searchLink><br /><searchLink fieldCode="DE" term="%22Psychology+of+children+with+disabilities%22">Psychology of children with disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Peer+pressure%22">Peer pressure</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Socioeconomic+status%22">Socioeconomic status</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Interviewing%22">Interviewing</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+of+mental+disorders%22">Classification of mental disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+prevalence%22">Disease prevalence</searchLink><br /><searchLink fieldCode="DE" term="%22Anxiety%22">Anxiety</searchLink><br /><searchLink fieldCode="DE" term="%22Attitudes+toward+disabilities%22">Attitudes toward disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Self-mutilation%22">Self-mutilation</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Bullying%22">Bullying</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Child+psychology%22">Child psychology</searchLink><br /><searchLink fieldCode="DE" term="%22Discrimination+against+people+with+disabilities%22">Discrimination against people with disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+disabilities%22">Learning disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Social+classes%22">Social classes</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Child+behavior%22">Child behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Saudi+Arabia%22">Saudi Arabia</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Clinical Psychology & Psychotherapy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/cpp.70245 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Self-injurious behavior Type: general – SubjectFull: Risk assessment Type: general – SubjectFull: Cross-sectional method Type: general – SubjectFull: Self-evaluation Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Emotion regulation Type: general – SubjectFull: Predictive tests Type: general – SubjectFull: Psychology of children with disabilities Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Peer pressure Type: general – SubjectFull: Research funding Type: general – SubjectFull: Logistic regression analysis Type: general – SubjectFull: Socioeconomic status Type: general – SubjectFull: Questionnaires Type: general – SubjectFull: Interviewing Type: general – SubjectFull: Descriptive statistics Type: general – SubjectFull: Classification of mental disorders Type: general – SubjectFull: Disease prevalence Type: general – SubjectFull: Anxiety Type: general – SubjectFull: Attitudes toward disabilities Type: general – SubjectFull: Self-mutilation Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Research methodology Type: general – SubjectFull: Bullying Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Comparative studies Type: general – SubjectFull: Data analysis software Type: general – SubjectFull: Child psychology Type: general – SubjectFull: Discrimination against people with disabilities Type: general – SubjectFull: Learning disabilities Type: general – SubjectFull: Social classes Type: general – SubjectFull: Mental depression Type: general – SubjectFull: Child behavior Type: general – SubjectFull: Children Type: general – SubjectFull: Saudi Arabia Type: general Titles: – TitleFull: Prediction and Prevalence of Self‐Harm and Nonsuicidal Self‐Injury in Children With Learning Disabilities: A Machine‐Learning Approach in Saudi Arabia. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Almulla, Mazen Omar – PersonEntity: Name: NameFull: Almulla, Abdullah Ahmed – PersonEntity: Name: NameFull: Khasawneh, Mohamad Ahmad Saleem IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10633995 Numbering: – Type: volume Value: 33 – Type: issue Value: 2 Titles: – TitleFull: Clinical Psychology & Psychotherapy Type: main |
| ResultId | 1 |
