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

Depression and Mood Disorders; Suicide Prevention (1290) Machine Learning (1401) Artificial Intelligence (1216) Methods and New Tools in Mental Health Research (428) Users' and Patients' Needs for Mental Health Services (382)

Published on in Vol 10 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42420, first published .
Prediction of Mental Health Problem Using Annual Student Health Survey: Machine Learning Approach

Prediction of Mental Health Problem Using Annual Student Health Survey: Machine Learning Approach

Prediction of Mental Health Problem Using Annual Student Health Survey: Machine Learning Approach

Authors of this article:

Ayako Baba1 Author Orcid Image ;   Kyosuke Bunji2 Author Orcid Image
Article Authors Cited by (8) Tweetations (2) Metrics

Journals

  1. Daza A, Arroyo-Paz , Bobadilla J, Apaza O, Pinto J. Stacking ensemble learning model for predict anxiety level in university students using balancing methods. Informatics in Medicine Unlocked 2023;42:101340 View
  2. Daza A, Saboya N, Necochea-Chamorro J, Zavaleta Ramos K, Vásquez Valencia Y. Systematic review of machine learning techniques to predict anxiety and stress in college students. Informatics in Medicine Unlocked 2023;43:101391 View
  3. Noreen R, Zafar A, Waheed T, Wasim M, Ahad A, Coelho P, Pires I. Unraveling the inner world of PhD scholars with sentiment analysis for mental health prognosis. Behaviour & Information Technology 2023:1 View
  4. Zhang M, Yan K, Chen Y, Yu R. Anticipating interpersonal sensitivity: A predictive model for early intervention in psychological disorders in college students. Computers in Biology and Medicine 2024;172:108134 View
  5. Ku W, Min H. Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors. Healthcare 2024;12(6):625 View
  6. Su Z, Liu R, Zhou K, Wei X, Wang N, Lin Z, Xie Y, Wang J, Wang F, Zhang S, Zhang X. Exploring the relationship between response time sequence in scale answering process and severity of insomnia: A machine learning approach. Heliyon 2024;10(13):e33485 View