Prediction of non-suicidal self-injury (NSSI) among rural Chinese junior high school students: a machine learning approach.
Aims: Non-suicidal self-injury (NSSI) is a serious issue that is increasingly prevalent among children and adolescents, especially in rural areas. Developing a suitable predictive model for NSSI is crucial for early identification and intervention. Methods: This study included 2090 Chinese rural chi...
| Published in: | Annals of General Psychiatry Vol. 23; no. 1; pp. 1 - 14 |
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| Main Authors: | , , , , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
BioMed Central
12/6/2024
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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=181495160&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181495160 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1744859X 1CFB jtl: Annals of General Psychiatry issn: 1744859X maglogo: N pubinfo: dt: 12/6/2024 vid: 23 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 181495160 181495160 181495160 10.1186/s12991-024-00534-w 181495160 ppf: 1 ppct: 13 formats: tig: atl: Prediction of non-suicidal self-injury (NSSI) among rural Chinese junior high school students: a machine learning approach. aug: au: Jiang, Zhongliang Cui, Yonghua Xu, Hui Abbey, Cody Xu, Wenjian Guo, Weitong Zhang, Dongdong Liu, Jintong Jin, Jingwen Li, Ying affil: Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China sug: subj: Injuries, Self-Inflicted Epidemiology Students, High School Psychosocial Factors Rural Population In Adolescence Prediction Models Machine Learning Human China Female Male Child Adolescence Cross Sectional Studies Scales Chinese Persons Psychosocial Factors Anxiety Depression Home Environment Questionnaires Surveys Sociodemographic Factors Support Vector Machine Decision Trees Random Forest Prevalence Psychological Tests Conflict (Psychology) Rural Areas Sensitivity and Specificity ROC Curve Data Analysis Software Descriptive Statistics Funding Source Child: 6-12 years Adolescent: 13-18 years Female Male ab: Aims: Non-suicidal self-injury (NSSI) is a serious issue that is increasingly prevalent among children and adolescents, especially in rural areas. Developing a suitable predictive model for NSSI is crucial for early identification and intervention. Methods: This study included 2090 Chinese rural children and adolescents. Participants' sociodemographic information, symptoms of anxiety as well as depression, personality traits, family environment and NSSI behaviors were collected through a questionnaire survey. Gender, age, grade, and all survey results except sociodemographic information were used as relevant factors for prediction. Support vector machines, decision tree and random forest models were trained and validated by the train set and valid set, respectively. The metrics of each model were tested and compared to select the most suitable one. Furthermore, the mean decrease Gini index was calculated to measure the importance of relevant factors. Results: The prevalence of NSSI was 38.3%. Out of the 6 models assessed, the random forest model demonstrated the highest suitability in predicting the prevalence of NSSI. It achieved sensitivity, specificity, AUC, accuracy, precision, and F1 scores of 0.65, 0.72, 0.76, 0.70, 0.57, and 0.61, respectively. Anxiety and depression were the top two contributing factors in the prediction model. Neuroticism and conflict were the factors that contributed the most to personality traits and family environment, respectively, in terms of prediction. In addition, demographic factors contributed little to the prediction in this study. Conclusion: This study focused on Chinese children and adolescents in rural areas and demonstrated the potential of using machine learning approaches in predicting NSSI. Our research complements the application of machine learning methods to psychiatric and psychological problems. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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