This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (744) Machine Learning (2076) Anxiety and Stress Disorders (1330) Mental Health Issues in Adolescence (637) Depression and Mood Disorders; Suicide Prevention (1691)

Published on in Vol 6, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32736, first published .
Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Authors of this article:

Radwan Qasrawi1, 2 Author Orcid Image ;   Stephanny Paola Vicuna Polo3 Author Orcid Image ;   Diala Abu Al-Halawa4 Author Orcid Image ;   Sameh Hallaq5 Author Orcid Image ;   Ziad Abdeen4 Author Orcid Image
Article Authors Cited by (45) Tweetations (4) Metrics

Journals

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  19. Mohammadi T, Orouei S, Parastouei K, Shahraki H, Parandeh A, Amini H, Raei M. Predicting Stress, Anxiety, and Depression in Adult Men Based on Nutritional and Lifestyle Variables: A Comparative Analysis of Machine Learning Methods. Journal of Food Science 2025;90(6) View
  20. Yu II E, Pineda E, Tano I, Lagman A, Victoriano J. Comparative analysis of supervised machine learning algorithms for predicting student programming anxiety levels. Journal of Artificial Intelligence, Machine Learning and Neural Network 2025;(51):28 View
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Books/Policy Documents

  1. Magboo M, Magboo V. Innovation in Medicine and Healthcare. View
  2. Rathiya R, Perumal L, Ramya R, Krithika R, Devatharshini S, Vyshnavi R. Innovations in Cybersecurity and Data Science. View
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Conference Proceedings

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  6. Sneha , Bhatia S, Batra M. 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). A Comparative Study of Machine Learning Algorithms in Predicting Mental Disorders View
  7. Wang X, Tang B. Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Education. Predicting Sensory Integration Disorder in 3- to 6-Year-Old Children: Application of Machine Learning Models View
  8. Kansara N, Vala B, Shaikh M. 2025 Eleventh International Conference on Bio Signals, Images, and Instrumentation (ICBSII). Machine Learning in Mental Health: Bridging Gaps in Diagnosis and Intervention View
  9. Zaman Z, Nova T, Riya L, Sarker T, Easha I, Akter E, Khan F. 2024 27th International Conference on Computer and Information Technology (ICCIT). Machine Learning Based Depression Prediction: Comparative Analysis of Models and Stacked Ensemble Approach View
  10. Prathima C, Shashidhar M, Pallavi G, Sohail K, Ehsanulla Basha S, Priya K. 2025 7th International Conference on Signal Processing, Computing and Control (ISPCC). A Machine Learning Approach on Multimedia Data for Predicting Student Anxiety View
  11. Gajbhiye N, Singh K. 2025 6th International Conference on Recent Advances in Information Technology (RAIT). An Optimized Depression Detection Technique Using Behavioral Analysis and Machine Learning View
  12. Hong Y, Xia Z. Proceedings of the 2025 International Conference on Artificial Intelligence and Smart Manufacturing. AI-Driven Innovations in Psychological Assessment: Multimodal Data, Intelligent Analytics, and Ethical Challenges View
  13. Kaushik D, Yadavalli R. 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON). Federated Explainable Mental Health Analytics (FEMHA): A Sustainable Framework for SDG-Aligned Risk Prediction and Emerging Challenges View