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

Development and Evaluation of Research Methods, Instruments and Tools (514) Machine Learning (1505) Anxiety and Stress Disorders (1081) Mental Health Issues in Adolescence (457) Depression and Mood Disorders; Suicide Prevention (1393)

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 (16) Tweetations (5) Metrics

Journals

  1. Santos I, Ralin V, Menezes E, Medeiros J. ANXIETY AND ITS RELATIONSHIP WITH LEARNING DISORDERS IN CHILDHOOD: A SYSTEMATIC REVIEW. Revista Contemporânea 2023;3(5):3539 View
  2. Qasrawi R, Vicuna Polo S, Abu Khader R, Abu Al-Halawa D, Hallaq S, Abu Halaweh N, Abdeen Z. Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments. Frontiers in Psychiatry 2023;14 View
  3. Irfan M, Shaf A, Ali T, Zafar M, Rahman S, I. Hendi M, M. Baeshen S, Maghfouri M, Alahmari H, Shahhar F, Shahhar N, Halawi A, Mahnashi F, Alqhtani S, Ali M. B, V E S. An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff. PLOS ONE 2023;18(6):e0286155 View
  4. Jin Y, Xu S, Shao Z, Luo X, Wang Y, Yu Y, Wang Y. Discovery of depression-associated factors among childhood trauma victims from a large sample size: Using machine learning and network analysis. Journal of Affective Disorders 2024;345:300 View
  5. Zhdan V, Holovanova I, Wang S, Obrevko N, Korneta O, Bіelikova I, Kaidashev I, Haque U, Khorosh M, Popovich I. ANALYSIS OF BEHAVIORAL FACTORS AND LEVEL OF ANXIETY OF SCHOOLCHILDREN IN THE CONDITIONS OF THE RUSSIAN-UKRAINIAN WAR. The Medical and Ecological Problems 2023;27(5-6):51 View
  6. Choi J, Kim K, Park S, Hur J, Yang H, Kim Y, Lee H, Han S. Investigation of factors regarding the effects of COVID-19 pandemic on college students’ depression by quantum annealer. Scientific Reports 2024;14(1) View
  7. López Steinmetz L, Sison M, Zhumagambetov R, Godoy J, Haufe S. Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine. Frontiers in Psychiatry 2024;15 View
  8. Wisidagama N, Marikar F, Sirisuriya M. A COMPREHENSIVE REVIEW ON SUITABLE IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUE FOR DISEASE IDENTIFICATION OF TOMATO AND POTATO PLANTS. Automation of technological and business processes 2024;16(1):29 View
  9. Almadhor A, Abbas S, Sampedro G, Alsubai S, Ojo S, Hejaili A, Strazovska L. Multi-Class Adaptive Active Learning for Predicting Student Anxiety. IEEE Access 2024;12:58097 View
  10. Monroy-Iglesias M, Russell B, Martin S, Fox L, Moss C, Bruno F, Millwaters J, Steward L, Murtagh C, Cargaleiro C, Bater D, Lavelle G, Simpson A, Onih J, Haire A, Reeder C, Jones G, Smith S, Santaolalla A, Van Hemelrijck M, Dolly S. Anxiety and depression in patients with non-site-specific cancer symptoms: data from a rapid diagnostic clinic. Frontiers in Oncology 2024;14 View

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