Stacked artificial neural network to predict the mental illness during the COVID-19 pandemic.

Abstract

The individual's mental health crisis and the COVID-19 pandemic lead to mental disorders. The transmission of the COVID-19 virus is associated with the levels of anxiety, stress, and depression in individuals, similar to other factors. Increases in mental illness cases and the prevalence of depression have peaked after the pandemic struck. The limited social intervention, reduced communication, peer support, and increased social isolation during the pandemic resulted in higher levels of depression, stress, and anxiety which leads to mental illness. Physiological distress is associated with the mental disorders, and its negative impact can be improved mainly by early detection and treatment. Early identification of mental illness is crucial for timely intervention to decelerate disorder severity and lessen individual health burdens. Laboratory tests for diagnosing mental illness depend on the self-reports of one's mental status, but it is labor intensive and time consuming. Traditional methods like linear or nonlinear regression cannot include many explanatory variables as they are prone to overfitting. The main challenge of the state-of-the-art models is the poor performance in detecting mental illnesses at early stages. Deep learning models can handle numerous variables. The current study focuses on demographic background, Kessler Psychological Distress, Happiness, and Health determinants of mental health during the pandemic to predict the mental health. This study's prediction can help rapid diagnosis and treatment and promote overall public mental health. Despite potential response bias, these proportions are exceptionally elevated, and it's plausible that certain individuals face an even higher level of risk. In the context of the COVID-19 pandemic, an investigation into mental health patients revealed a disproportionate representation of children and individuals with neurotic disorders among those articulating substantial or severe apprehensions.