Evidence map›Paper›PMID 41372985›Full record

ArticleBMC psychology2025

Silent struggles: a machine learning approach for predicting suicidal ideation based on crisis symptoms and childhood trauma in Saudi adolescents.

Mogeda El Sayed El Keshky, Radeah Mohammed Hamididin

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Article in BMC psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

2 authors.

Mogeda El Sayed El KeshkyDepartment of Psychology, Faculty of Arts and Humanities, King Abdulaziz University, Jeddah, 42803, Kingdom of Saudi Arabia. drmogeda@gmail.com.ORCID http://orcid.org/0000-0003-0456-9889
Radeah Mohammed HamididinDepartment of Psychology, Faculty of Arts and Humanities, King Abdulaziz University, Jeddah, 42803, Kingdom of Saudi Arabia.ORCID http://orcid.org/0000-0002-2160-2823

Funding

The National Centre for Social Research (NCSS) in Saudi Arabia ن ع - 46 - 1
6 · The paper itself

Abstract

backgroundAdolescent suicide is a significant public health concern, with rising rates globally and increasing psychological distress among youth. Despite its significance, limited research in Saudi Arabia has explored the combined impact of acute emotional crises and childhood trauma on suicidal ideation, particularly using machine learning approach.

objectivesThis study aimed to identify key psychological and trauma-related predictors of suicidal ideation among Saudi adolescents. It specifically examined the role of Suicidal Crisis Syndrome symptoms and childhood maltreatment experiences using both correlational and machine learning approaches.

methodsA cross-sectional study was conducted among 583 Saudi adolescents aged 13–18 years, using convenience sampling. Participants completed a battery of validated self-report instruments: the Suicide Crisis Inventory, the Childhood Trauma Questionnaire–Short Form, and the Okasha Suicidality Scale. Descriptive statistics, bivariate correlations, and variable importance analysis via machine learning, in particular a random forest classifier, were used to identify the strongest predictors of suicidal ideation.

resultsEmotional pain, entrapment, and panic dissociation were the most significant predictors of suicidal ideation. Childhood trauma variables—particularly emotional abuse, sexual abuse, and emotional neglect—also showed strong positive correlations with suicidal ideation. Adolescents from divorced families and those in high school reported significantly higher suicidal ideation scores. The random forest model confirmed emotional pain and entrapment as top predictors.

conclusionsSuicidal ideation among Saudi adolescents is strongly influenced by both acute emotional crisis symptoms and histories of childhood trauma. These findings highlight the need for early screening of suicidal crisis markers and trauma-informed mental health interventions in school and clinical settings. Culturally sensitive strategies that integrate emotional, developmental, and family-based factors are essential for effective suicide prevention among adolescents.

Indexed as

Adverse Childhood ExperiencesChild AbuseMachine LearningSuicidal IdeationAdolescentCross-Sectional StudiesFemaleHumansMalePredictive Learning ModelsSaudi ArabiaAdolescentsChildhood traumaEmotional painEntrapmentMachine learningSaudi arabiaSuicidal ideationSuicide crisis syndrome

Identifiers

PMID41372985
PMCPMC12801999

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