ArticleJMIR formative research2022
Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis.
Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- AI-Based Approaches to Refugee Mental Health Care: Systematic Integrative Review.Journal of medical Internet research · 2026Pooled it
- Methodological guidance on clinical prediction models in mental health research.Psychological medicine · 2026Review
- Impact of the Interaction Between Screen Time and Activity Interests on Adolescent Depression Risk: Construction of a Predictive Model Based on Machine Learning.Actas espanolas de psiquiatria · 2026Article
- Using Machine Learning to Predict Depression among Adolescents Living with HIV in Uganda.Global social welfare : research, policy & practice · 2026Article
- Cultural validation of the RCADS and use of ensemble learning for symptom profiling of anxiety and depression.Frontiers in psychiatry · 2026Article
- Contribution of social determinants to symptoms of generalized anxiety disorder.PLOS mental health · 2026Article
- Predicting 3-year depressive symptoms among middle-aged and older adults in rural China using random forest: insights from the China health and retirement longitudinal study.BMC psychology · 2025Article
- Predicting depression risk with machine learning models: identifying familial, personal, and dietary determinants.BMC psychiatry · 2025Article
- Use of Artificial Intelligence in Adolescents' Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions.JMIR mental health · 2025Article
- Explainable Machine Learning in the Prediction of Depression.Diagnostics (Basel, Switzerland) · 2025Article
- Apriori algorithm based prediction of students' mental health risks in the context of artificial intelligence.Frontiers in public health · 2025Article
- Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer.Scientific reports · 2024Article
- Anxiety and depression in patients with non-site-specific cancer symptoms: data from a rapid diagnostic clinic.Frontiers in oncology · 2024Article
- Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine.Frontiers in psychiatry · 2024Article
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff.PloS one · 2023Article
- Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments.Frontiers in psychiatry · 2023Article
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDepression and anxiety symptoms in early childhood have a major effect on children's mental health growth and cognitive development. The effect of mental health problems on cognitive development has been studied by researchers for the last 2 decades.
objectiveIn this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren's depression and anxiety.
methodsThe study sample consisted of 3984 students in fifth to ninth grades, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors schoolchildren questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. We used 5 machine learning techniques (random forest [RF], neural network, decision tree, support vector machine [SVM], and naive Bayes) for prediction.
resultsThe results indicated that the SVM and RF models had the highest accuracy levels for depression (SVM: 92.5%; RF: 76.4%) and anxiety (SVM: 92.4%; RF: 78.6%). Thus, the SVM and RF models had the best performance in classifying and predicting the students' depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting the depression and anxiety scales.
conclusionsOverall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The machine learning techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students' mental health and cognitive development.
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