Assessing Mental Health Crisis in Pandemic Situation With Computational Intelligence
Abstract
Covid-19 pandemic has created huge emotional distress and increased the risk of psychiatric problems. This happened owing to imposition of necessary stringent healthcare measures that infringed personal space, emotional freedom, and caused financial loss. Our physical well-being is directly associated with mental fitness and health. From analysis it has been found that feature like struggling in concentration and memory, visionary issues, and arthritis are customary symptoms in patients suffering from mental crises. Our proposed research work aims to find out the reasons behind mental illness and ways to improve mental disorders using supervised approach. The main focus is to develop a smart computationally intelligent model to assist healthcare practitioners in analysing and diagnosing severe mental illness. Our proposedmodel assists in analysing causes of mental disorder and aids in reducing total medicinal cost along with reduced mental illness rate. Additionally, a recommendation system is also developed for diagnosing depressive patients.
Full Text:
PDFReferences
Pfefferbaum, Betty, and Carol S. North. "Mental health and the Covid-19 pandemic." New England Journal of Medicine 383, no. 6 (2020): 510-512.
Schäfer, Sarah K., M. Roxanne Sopp, Christian G. Schanz, Marlene Staginnus, Anja S. Göritz, and Tanja Michael. "Impact of COVID-19 on public mental health and the buffering effect of a sense of coherence." Psychotherapy and Psychosomatics 89, no. 6 (2020): 386-392.
Bish, Connie L., Heidi MichelsBlanck, Mary K. Serdula, Michele Marcus, Harold W. Kohl III, and Laura Kettel Khan. "Diet and physical activity behaviors among Americans trying to lose weight: 2000 Behavioral Risk Factor Surveillance System." Obesity research 13, no. 3 (2005): 596-607.
Centers for Disease Control and Prevention. "Behavioral risk factor surveillance system questionnaire." System 83, no. 12 (2011): 76.
Brooks, Samantha K., Rebecca K. Webster, Louise E. Smith, Lisa Woodland, Simon Wessely, Neil Greenberg, and Gideon James Rubin. "The psychological impact of quarantine and how to reduce it: rapid review of the evidence." The Lancet 395, no. 10227 (2020): 912-920.
Cortez, Pedro Afonso, Shijo John Joseph, Nileswar Das, Samrat Singh Bhandari, and Sheikh Shoib. "Tools to measure the psychological impact of the COVID-19 pandemic: What do we have in the platter?." Asian Journal of Psychiatry 53 (2020): 102371.
Chawla, Nitesh V., Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. "SMOTE: synthetic minority over-sampling technique." Journal of Artificial Intelligence Research 16 (2002): 321-357.
Ramentol, Enislay, Yailé Caballero, Rafael Bello, and Francisco Herrera. "SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and under sampling for high imbalanced data-sets using SMOTE and rough sets theory." Knowledge and information systems 33, no. 2 (2012): 245-265.
Giraldo-Forero, Andrés Felipe, Jorge Alberto Jaramillo-Garzón, José Francisco Ruiz-Muñoz, and César Germán Castellanos-Domínguez. "Managing imbalanced data sets in multi-label problems: a case study with the SMOTE algorithm." In Iberoamerican Congress on Pattern Recognition, pp. 334-342. Springer, Berlin, Heidelberg, 2013.
Simon, Gregory, Johan Ormel, Michael Von Korff, and William Barlow. "Health care costs associated with depressive and anxiety disorders in primary care." American Journal of Psychiatry 152, no. 3 (1995): 352-357.
Simon, Gregory E., Michael Von Korff, and William Barlow. "Health care costs of primary care patients with recognized depression." Archives of general psychiatry 52, no. 10 (1995): 850-856.
Ciechanowski, Paul S., Wayne J. Katon, and Joan E. Russo. "Depression and diabetes: impact of depressive symptoms on adherence, function, and costs." Archives of internal medicine 160, no. 21 (2000): 3278-3285.
Barua, Sukarna, Md Monirul Islam, Xin Yao, and Kazuyuki Murase. "MWMOTE--majority weighted minority oversampling technique for imbalanced data set learning." IEEE Transactions on knowledge and data engineering 26, no. 2 (2012): 405-425.
Barua, Sukarna, Md Monirul Islam, and Kazuyuki Murase. "A novel synthetic minority oversampling technique for imbalanced data set learning." In International Conference on Neural Information Processing, pp. 735-744. Springer, Berlin, Heidelberg, 2011.
Tong, Simon, and Daphne Koller. "Support vector machine active learning with applications to text classification." Journal of machine learning research 2, no. Nov (2001): 45-66.
Fatima, Meherwar, and Maruf Pasha. "Survey of machine learning algorithms for disease diagnostic." Journal of Intelligent Learning Systems and Applications 9, no. 01 (2017): 1.
T. Kolenik and M. Gams, "Persuasive Technology for Mental Health: One Step Closer to (Mental Health Care) Equality?," in IEEE Technology and Society Magazine, vol. 40, no. 1, pp. 80-86, March 2021, doi: 10.1109/MTS.2021.3056288.
Kolenik, T. (2022). Methods in digital mental health: smartphone-based assessment and intervention for stress, anxiety, and depression. In Integrating Artificial Intelligence and IoT for Advanced Health Informatics (pp. 105-128). Springer, Cham.
K. Nigam, K. Godani, D. Sharma, S. Khandelwal and M. Rathi, "Personalised Heart Monitoring and Reporting System," 2020 Research, Innovation, Knowledge Management and Technology Application for Business Sustainability (INBUSH), 2020, pp. 68-73, doi: 10.1109/INBUSH46973.2020.9392184.
Rathi, M., Sahu, S., Goel, A., & Gupta, P. (2022). Personalized Health Framework for Visually Impaired. Informatica, 46(1).
Gautam, A., Chauhan, A. S., Srivastava, A., Jadon, C., & Rathi, M. (2019). Major Histocompatibility Complex Binding and Various Health Parameters Analysis. In Smart Healthcare Systems (pp. 151-164). CRC Press.
Rathi, M., Mittal, A., & Agarwal, D. (2020, February). Prediction of Thorax Diseases Using Deep and Transfer Learning. In 2020 Research, Innovation, Knowledge Management and Technology Application for Business Sustainability (INBUSH) (pp. 236-240). IEEE.
Rathi, M., & Pareek, V. (2016). Disease prediction tool: an integrated hybrid data mining approach for healthcare. IRACST Int J Comput Sci Inf Technol Secur (IJCSITS), 6(6), 32-40.
O. Oyebode, F. Alqahtani and R. Orji, "Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews," in IEEE Access, vol. 8, pp. 111141-111158, 2020, doi: 10.1109/ACCESS.2020.3002176.
E. Gore and S. Rathi, "Surveying Machine Learning Algorithms On Eeg Signals Data For Mental Health Assessment," 2019 IEEE Pune Section International Conference (PuneCon), 2019, pp. 1-6, doi: 10.1109/PuneCon46936.2019.9105749.
Sabourin, A. A., Prater, J. C., & Mason, N. A. (2019). Assessment of mental health in doctor of pharmacy students. Currents in Pharmacy Teaching and Learning, 11(3), 243-250.
Hou, Y., Xu, J., Huang, Y., & Ma, X. (2016, November). A big data application to predict depression in the university based on the reading habits. In 2016 3rd International Conference on Systems and Informatics (ICSAI) (pp. 1085-1089). IEEE.
Gokten, E. S., & Uyulan, C. (2021). Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier. Journal of Affective Disorders, 279, 256-265.
Xin, Y., Ren, X. Predicting depression among rural and urban disabled elderly in China using a random forest classifier. BMC Psychiatry 22, 118 (2022). https://doi.org/10.1186/s12888-022-03742-4.
Srividya, M., Mohanavalli, S., & Bhalaji, N. (2018). Behavioral modeling for mental health using machine learning algorithms. Journal of medical systems, 42(5), 1-12.
Tate, A. E., McCabe, R. C., Larsson, H., Lundström, S., Lichtenstein, P., & Kuja-Halkola, R. (2020). Predicting mental health problems in adolescence using machine learning techniques. PloS one, 15(4), e0230389.
Reddy, U. S., Thota, A. V., & Dharun, A. (2018). Machine learning techniques for stress prediction in working employees. In 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1-4). IEEE.
Potter, Gail, Jimmy Wong, Irvin Alcaraz, and Peter Chi. "Web application teaching tools for statistics using R and shiny." Technology Innovations in Statistics Education 9, no. 1 (2016).
Conway, Jake R., Alexander Lex, and Nils Gehlenborg. "UpSetR: an R package for the visualization of intersecting sets and their properties." Bioinformatics 33, no. 18 (2017): 2938-2940.
Chintalapudi, Nalini, Gopi Battineni, Marzio Di Canio, Getu Gamo Sagaro, and Francesco Amenta. "Text mining with sentiment analysis on seafarers’ medical documents." International Journal of Information Management Data Insights 1, no. 1 (2021): 100005.
Adwitiya Sinha, “PSIR: A Novel Phase-wise Diffusion Model for Lockdown Analysis of COVID-19 Pandemic in India,” System Assurance Engineering & Management, Springer, pp. 1-17, October 2021
Ramanna, Sheela, and Lakhmi C. Jain. Emerging paradigms in machine learning. Edited by Robert J. Howlett. Heidelberg: Springer, 2013.
Sinha, A., & Rathi, M. (2021). COVID-19 prediction using AI analytics for South Korea. Applied Intelligence, 51(12), 8579-8597.
Sinha, A. (2021). PSIR: a novel phase-wise diffusion model for lockdown analysis of COVID-19 pandemic in India. International Journal of System Assurance Engineering and Management, Springer, 1-14.
Saxena, N., Chahal, E. S., Sinha, A., & Chand, S. (2021). Coronavirus Infection Segmentation & Detection Using UNET Deep Learning Architecture. In 2021 IEEE 18th India Council International Conference (INDICON), pp. 1-6.
DOI: https://doi.org/10.31449/inf.v47i1.3902
This work is licensed under a Creative Commons Attribution 3.0 License.