Assessment and prediction of depression and anxiety risk factors in schoolchildren: machine learning techniques performance analysis

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dc.authoridRadwan Qasrawi / 0000-0001-8671-7026
dc.authorscopusidRadwan Qasrawi / 57212263325
dc.authorwosidRadwan Qasrawi / JMG-1470-2023
dc.contributor.authorQasrawi, Radwan F.
dc.contributor.authorPolo, Stephanny Paola Vicuna
dc.contributor.authorAl-Halawa, Diala Abu
dc.contributor.authorHallaq, Sameh
dc.contributor.authorAbdeen, Ziad A.
dc.date.accessioned2022-11-07T06:49:37Z
dc.date.available2022-11-07T06:49:37Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractBackground: Depression 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. Objective: In this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren's depression and anxiety. Methods: The 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. Results: The 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. Conclusions: Overall, 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.en_US
dc.identifier.citationQasrawi, R., Polo, S. P. V., Al-Halawa, D. A., Hallaq, S., & Abdeen, Z. (2022). Assessment and prediction of depression and anxiety risk factors in schoolchildren: Machine learning techniques performance analysis. JMIR Formative Research, 6(8) doi:10.2196/32736en_US
dc.identifier.doi10.2196/32736en_US
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85139152600en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.2196/32736
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3231
dc.identifier.volume6en_US
dc.identifier.wosWOS:000854086000066en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorQasrawi, Radwan
dc.language.isoenen_US
dc.publisherJMIR Publications Inc.en_US
dc.relation.ispartofJMIR Formative Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnxietyen_US
dc.subjectChildrenen_US
dc.subjectDepressionen_US
dc.subjectEarly Childhood Educationen_US
dc.subjectMachine Learningen_US
dc.subjectPredictionen_US
dc.subjectRandom Foresten_US
dc.subjectSchool-age Childrenen_US
dc.subjectSchoolchildrenen_US
dc.subjectRansition-aged Youthen_US
dc.subjectYoung Adulten_US
dc.subjectYouthen_US
dc.titleAssessment and prediction of depression and anxiety risk factors in schoolchildren: machine learning techniques performance analysisen_US
dc.typeArticleen_US

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