Associating Unemployment with Panic Attack Using Deep Learning during COVID-19
Shawni Dutta
Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.
Samir Kumar Bandyopadhyay *
The Bhawanipur Education Society College, Kolkata, India.
*Author to whom correspondence should be addressed.
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.