Associating Unemployment with Panic Attack Using Deep Learning during COVID-19

Main Article Content

Shawni Dutta
Samir Kumar Bandyopadhyay

Abstract

Corona Virus Infectious Disease (COVID-19) is newly emerging infectious disease. It is known to the world in late 2019. Due to this, the mental health of employees is disturbed. There is always a fear of unemployment amongst employees due to the present scenario of lockdown. This may even create a panic attack. It has been happening rapidly during COVID-19. It has a great effect on human health. This paper analyses multiple factors that have an impact on causing panic attacks. Deep Learning techniques are explored which detects panic disorders on people. Recurrent Neural Network (RNN) based deep learning framework is utilized in this paper that assembles multiple RNN layers along with other hyper-parameters into a single model. This method is implemented by capturing interfering factors and predicts the panic attack tendency of people during COVID-19. Early prediction of panic attacks may assist in saving life from unwanted circumstances. It is also observed that comparative study between MLP and stacked-RNN classifier indicates significantly better results of proposed model over MLP classifier in terms of evaluating metrics.

Keywords:
Panic disorder, classifier, unemployment rate, deep learning, mental illness, COVID-19.

Article Details

How to Cite
Dutta, S., & Bandyopadhyay, S. K. (2020). Associating Unemployment with Panic Attack Using Deep Learning during COVID-19. Journal of Engineering Research and Reports, 17(3), 33-40. https://doi.org/10.9734/jerr/2020/v17i317190
Section
Original Research Article

References

World Health Organization. WHO Statement regarding cluster of pneumonia cases in Wuhan, China; 2020. Available:https://www.who.int/china/news/detail/09-01-2020-who-statement-regarding-cluster-of-pneumonia-cases-in-wuhan-china

Weich S, Lewis G. Poverty, unemployment, and common mental disorders: Population based cohort study. Br. Med. J. 1998;317(7151):115–119. DOI: 10.1136/bmj.317.7151.115

Jefferis BJ. et al. Associations between unemployment and major depressive disorder: Evidence from an international, prospective study (the predict cohort),” Soc. Sci. Med. 2011;73(11):1627–1634. DOI: 10.1016/j.socscimed.2011.09.029

Liu J, Liu J, Du W, Li D. Performance analysis and characterization of training deep learning models on mobile device. Proc. Int. Conf. Parallel Distrib. Syst. - ICPADS. 2019;506–515. DOI: 10.1109/ICPADS47876.2019.00077

Sherstinsky A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D Nonlinear Phenom. 2020;404:1–43. DOI: 10.1016/j.physd.2019.132306

Yedida R, Reddy R, Vahi R, Jana R, GVA, Kulkarni D. Employee attrition prediction. arXiv:1806.10480 [stat.ML]; 2018.

Shankar RS, Rajanikanth J, Sivaramaraju VV, Vssr Murthy K. Prediction of employee attrition using datamining. IEEE Int. Conf. Syst. Comput. Autom. Networking, ICSCA 2018;1–8. DOI: 10.1109/ICSCAN.2018.8541242

Khare R, Kaloya D, Choudhary CK, Gupta G. Employee Attrition risk assessment using logistic regression analysis. Int. Conf. Adv. Data Anal. Bus. Anal. Intell. 2011;1–33.

Yadav S, Jain A, Singh D. Early prediction of employee attrition using data mining techniques. Proc. 8th Int. Adv. Comput. Conf. IACC. 2018;8(2882):349–354. DOI: 10.1109/IADCC.2018.8692137

Díaz-García A, et al. Pervasive Computing paradigms for mental health. Pervasive Comput. Paradig. Ment. Heal. 2019;604: 147–156. DOI: 10.1007/978-3-319-32270-4

Karthikeyan P, Murugappan M, Yaacob S. Analysis of stroop colorword test-based human stress detection using electrocardiography and heart rate variability signals. Arab. J. Sci. Eng. 2012; 39(3):1835–1847. DOI: 10.1007/s13369-013-0786-8

Strauss J, Peguero AM, Hirst G. Machine learning methods for clinical forms analysis in mental health. Stud. Health Technol. Inform. 2013;192(1–2):1024. DOI: 10.3233/978-1-61499-289-9-1024

KRC. et al. Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol. Psychiatry. 2016;21(10):1366–1371. DOI: 10.1038/mp.2015.198.Testing

Coşkun, Musab, et al. An Overview of Popular Deep Learning Methods. Eur. J. Tech. 2017;7(2):165–176. DOI: 10.23884/ejt.2017.7.2.11

Nwankpa C, Ijomah W, Gachagan A, Marshall S. Activation functions: Comparison of trends in practice and research for deep learning. 2018;1–20. DOI: arXiv:1811.03378 [cs.LG]

Lipton ZC, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning. 2015;1–38. DOI: arXiv:1506.00019 [cs.LG]

SHI, Shen Dinggang, Wu Gurrong. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 2017;19:221–248. DOI: 10.1146/annurev-bioeng-071516

Kingma DP, Ba JL. Adam: A method for stochastic optimization. 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc. 2015;1–15.

You Y, Hseu J, Ying C, Demmel J, Keutzer K, Hsieh CJ. Large-batch training for LSTM and beyond. Int. Conf. High Perform. Comput. Networking, Storage Anal. SC. 2019;1–15. DOI: 10.1145/3295500.3356137

Michael Corley. Unemployment and mental illness survey, Exploring the causation of high unemployment among the mentally ill. 2019;2. Available:https://www.kaggle.com/michaelacorley/unemployment-and-mental-illness-survey

Murtagh F. Multilayer perceptrons for classification and regression. Neurocom-puting. 1991;2(5–6):183–197.