Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis

Corona Virus Disease-2019 (COVID-19) was reported at first in Wuhan city, China by December 2019. World Health Organization (WHO) declared COVID-19 as a pandemic i.e., global health crisis on March 11, 2020. The outbreak of and subsequent lockdowns to curb the spread, not only affected economic status number countries, but it also resulted increased levels Depression, Anxiety, Stress (DAS) among people. Therefore, there is need exists comprehend relationship psycho-social factors country that hypothetically high stress fear; with tremendously-limiting measures social distancing lockdown force; rates new cases mortalities. With this motivation, current study aims investigating DAS college students during since they are identified highly-susceptible population. proposes develop Intelligent Feature Subset Selection Machine Learning-based predictive (IFSSML-DAS) model. presented IFSSML-DAS model involves data preprocessing, (FSS), classification, parameter tuning. Besides, uses Group Gray Wolf Optimization based FSS (GGWO-FSS) technique reduce curse dimensionality. In addition, Beetle Swarm Least Square Support Vector (BSO-LSSVM) employed for classification which weight bias parameters LSSVM optimally adjusted using BSO algorithm. performance proposed tested benchmark DASS-21 dataset results were investigated under different measures. outcome suggests development specialized programs handle population so overcome crisis.