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

Depression and Mood Disorders; Suicide Prevention (1691) Machine Learning (2076) Artificial Intelligence (2129) Methods and New Tools in Mental Health Research (558) Users' and Patients' Needs for Mental Health Services (502)

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 (31) Tweetations (4) 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
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  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
  7. Vandana , Srivastava S, Arora N, Gupta V. An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders. International Research Journal of Multidisciplinary Technovation 2024:106 View
  8. Zhang L, Zhao S, Yang Z, Zheng H, Lei M. An artificial intelligence tool to assess the risk of severe mental distress among college students in terms of demographics, eating habits, lifestyles, and sport habits: an externally validated study using machine learning. BMC Psychiatry 2024;24(1) View
  9. Ding H, Li N, Li L, Xu Z, Xia W. Machine learning-enabled mental health risk prediction for youths with stressful life events: A modelling study. Journal of Affective Disorders 2025;368:537 View
  10. Muntean R, Stefanica V, Rosu D, Boncu A, Stoian I, Oravitan M. Examining the interplay between mental health indicators and quality of life measures among first-year law students: a cross-sectional study. PeerJ 2024;12:e18245 View
  11. Park N, Woo H. Change in lifestyle and mental health in young adults: an exploratory study with hybrid machine learning. Frontiers in Public Health 2025;13 View
  12. Wang Y. Prediction of Mental Health Problems Based on Logistic Regression Model. Highlights in Science, Engineering and Technology 2025;144:280 View
  13. Jin T, Halili A. Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data. Frontiers in Artificial Intelligence 2025;8 View
  14. Basha S, Gull M, Alquqa E, Mahmoud K, Harhash A. The role of AI in university students’ mental health: a bibliometric review. Discover Social Science and Health 2025;5(1) View
  15. Wongchaisuwat P, Kaewbundit V, Noomnual S. Application of machine learning to identify key factors influencing agricultural workers’ mental health: A case study of Thai farmers. Health Informatics Journal 2025;31(4) View
  16. Geng S, Wang J, Xia Y, Niu B, Deng X, Wu X. Predicting generalized anxiety disorder among Chinese depressed adolescents: an explainable machine learning approach. BMC Medical Informatics and Decision Making 2025;25(1) View

Books/Policy Documents

  1. Menon V, Sateesh Kumar T, Thomas L, Thomas J. Exploring Psychology, Social Innovation and Advanced Applications of Machine Learning. View
  2. Suma K, Sunitha G, Tabassum A, Reddy A, Meghana B, Reddy C, Galety M. Proceedings of Sixth International Conference on Computer and Communication Technologies. View
  3. Islas-Avila A, Martinez-Rebollar A, Castrejon-Salgado R, Avila-Jimenez L, Estrada-Esquivel H, Sanchez-Gutierrez A. Advances in Soft Computing. View

Conference Proceedings

  1. Pritam N, Gill K, Kumar M, Rawat R, Banerjee D. 2024 3rd International Conference for Innovation in Technology (INOCON). Classification of Student Mental Health Analysis using Logistic Regression and other classification techniques through Machine Learning Methods View
  2. Chen J. 2024 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE). Student Mental Health Risk Prediction Based on Apriori Algorithm in the Context of Big Data View
  3. Arias E, Parraga-Alava J, Montenegro D. 2024 Tenth International Conference on eDemocracy & eGovernment (ICEDEG). Stress Detection among Higher Education Students: A Comprehensive Systematic Review of Machine Learning Approaches View
  4. Hermawan L, Stiawan D, Syakurah R, Meilinda , Ikhsan D. 2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). Mental Health with Machine Learning: A Prediction-Based Intervention Chatbot for Mental Health Conversations View
  5. Sharma N, Patel I, Singh N, Mishra B. 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). Predictive Modelling of Mental Health Disorders Using Machine Learning Classifiers View
  6. Priya B, Chinnammal V, Srinu N, P. K S, Selvam M, Vijay S. 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). ENOL: A Robust Learning Based Methodology to Predict Mental Health Illness by Using Elevated Neural Optimization Logic View
  7. Gao Y, Li Y, Gao H, Ning Y. 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). Bidirectional Long Short-Term Memory based Student Common Disease Monitoring System View
  8. Saketh K, Kumar S H, Reddy P P, C J, K A. 2025 7th International Conference on Intelligent Sustainable Systems (ICISS). Insights into Mental Health Challenges in Digital Education using Suitable Machine Learning Techniques View
  9. S Y, G A. 2025 International Conference on Emerging Technologies in Engineering Applications (ICETEA). Machine Learning Framework for Accurate Mental Stress Detection in University Students Using Random Forest Classification View
  10. Abbas S, Al-Hamadani F, Sultana A, Nasir M, Garcia-Ruiz M, Lin W. 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI). Cost-Effective Predictive Modeling for Student Mental Health Using Readily-Available Data View
  11. 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
  12. Sutone R, Malhotra R. 2025 IEEE 6th India Council International Subsections Conference (INDISCON). Lifestyle and Behavioral Data-Driven Mental Health Prediction: Insights from Machine Learning Models View