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

作　　者：Fahd N.Al-Wesabi Hadeel Alsolai Anwer Mustafa Hilal Manar Ahmed Hamza Mesfer Al Duhayyim Noha Negm 

机构地区：[1]Department of Computer Science,King Khalid University,Muhayel Aseer,Saudi Arabia [2]Faculty of Computer and IT,Sana’a University,Sana’a,Yemen [3]Department of Information Systems,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,Saudi Arabia [4]Department of Computer and Self Development,Preparatory Year Deanship,Prince Sattam bin Abdulaziz University,AlKharj,Saudi Arabia [5]Department of Natural and Applied Sciences,College of Community-Aflaj,Prince Sattam bin Abdulaziz University,Saudi Arabia [6]Computer Science Department,King Khaled University,KSA [7]Faculty of Science,Mathematics and Computer Science Department,Menoufia University,Egypt

出　　处：《Computers, Materials & Continua》2022年第3期5803-5820,共18页计算机、材料和连续体（英文）

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 2/25/42),www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.

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

关 键 词：Psycho-social factors covid-19 crisis management predictive models decision making machine learning 

分 类 号：R563.1[医药卫生—呼吸系统] TP181[医药卫生—内科学]