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- Vol. 3 Issue 2
- Article
Mental Health Issues Prediction during Covid Pandemic Using Machine Learning
Authors
1234 Department of Information Technology, M.H. Saboo Siddik College of Engineering, Byculla, Mumbai – 400008, India.
Article Information
- DOI
- No DOI
- Published In
- Volume 3, Issue 2 (2022)
- Published Online
- March-April 2022
- Pages
- 124-127
- Article Type
- Research Article
- Check for updates
- No DOI available for cross-reference
References
[1] Mhambe Priscilla Dooshima, Egejuru Ngozi Chidozie, Balogun Jeremiah Ademola, Olusanya Olayinka Sekoni, Idowu Peter Adebayo. “A Predictive
Model for the Risk of Mental Illness in Nigeria Using Data Mining”. International Journal of Immunology. Vol. 6, No. 1, 2018, pp. 5-16. doi:
10.11648/j.iji.20180601.12
[2] U. S. Reddy, A. V. Thota and A. Dharun, "Machine Learning Techniques for Stress Prediction in Working Employees," 2018 IEEE International
Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, 2018, pp. 1-4
[3] Anu Priya, Shruti Garg, Neha Prerna Tigga, "Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms" 2019
International Conference on Computational Intelligence and Data Science (ICCIDS), Mesra, Ranchi-835215, India.
[4] Alonso, Susel Góngora; de la Torre-Díez, Isabel; Hamrioui, Sofiane; López-Coronado, Miguel; Barreno, Diego Calvo; Nozaleda, Lola Morón; Franco,
Manuel (2018). Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. Journal of Medical Systems, 42(9), 161–
. doi:10.1007/s10916-018-1018-2
[5] Li, Xiaowei; Zhang, Xin; Zhu, Jing; Mao, Wandeng; Sun, Shuting; Wang, Zihan; Xia, Chen; Hu, Bin (2019). Depression recognition using machine
learning methods with different feature generation strategies. Artificial Intelligence in Medicine, (), S0933365719300296–
. doi:10.1016/j.artmed.2019.07.004
[6] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473764/
[7] https://www.kaggle.com/kunal28chaturvedi/covid19-and-its-impact-on-students
[8] https://www.kaggle.com/rohan2002/mentalhealth-treatment prediction?select=tuned_mental_health_random.h5
[9] D.Filip & C. Jesus. (2015). A Neural Network Based Model for Predicting Psychological Conditions International Conference on Brain Informatics
and Health 252-261.
[10] https://pubmed.ncbi.nlm.nih.gov/32805704//
Model for the Risk of Mental Illness in Nigeria Using Data Mining”. International Journal of Immunology. Vol. 6, No. 1, 2018, pp. 5-16. doi:
10.11648/j.iji.20180601.12
[2] U. S. Reddy, A. V. Thota and A. Dharun, "Machine Learning Techniques for Stress Prediction in Working Employees," 2018 IEEE International
Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, 2018, pp. 1-4
[3] Anu Priya, Shruti Garg, Neha Prerna Tigga, "Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms" 2019
International Conference on Computational Intelligence and Data Science (ICCIDS), Mesra, Ranchi-835215, India.
[4] Alonso, Susel Góngora; de la Torre-Díez, Isabel; Hamrioui, Sofiane; López-Coronado, Miguel; Barreno, Diego Calvo; Nozaleda, Lola Morón; Franco,
Manuel (2018). Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. Journal of Medical Systems, 42(9), 161–
. doi:10.1007/s10916-018-1018-2
[5] Li, Xiaowei; Zhang, Xin; Zhu, Jing; Mao, Wandeng; Sun, Shuting; Wang, Zihan; Xia, Chen; Hu, Bin (2019). Depression recognition using machine
learning methods with different feature generation strategies. Artificial Intelligence in Medicine, (), S0933365719300296–
. doi:10.1016/j.artmed.2019.07.004
[6] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473764/
[7] https://www.kaggle.com/kunal28chaturvedi/covid19-and-its-impact-on-students
[8] https://www.kaggle.com/rohan2002/mentalhealth-treatment prediction?select=tuned_mental_health_random.h5
[9] D.Filip & C. Jesus. (2015). A Neural Network Based Model for Predicting Psychological Conditions International Conference on Brain Informatics
and Health 252-261.
[10] https://pubmed.ncbi.nlm.nih.gov/32805704//
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