Depression and Anxiety Analysis and Prediction using Big Data Technologies

Depression and Anxiety Analysis and Prediction using Big Data Technologies

Mersiha Ćeranić, Samed Jukić

COVID-19 pandemic brought many changes in people’s lifestyles. Some of those changes hurt people's mental health in different age groups. This research is done to investigate which factors contributed most to the occurrence of depressive and anxiety symptoms during COVID-19 lockdown, and what type of people in terms of age, sex, level of education, place of living, was the most exposed to the appearance of mental health disorders. 1115 people (18-85 years old) from Poland joined the research process. They fulfilled online questionnaires which were used as a basis for further research of lockdown impact on mental health. Responses are evaluated by using ML tools predicting the group of participants with signs of depression and
anxiety, based on their answers to the questionnaires, and the attributes of the participants. Based on the results given by the studies, the youngest population (age 18-29), which participated in the surveys, experienced more intense depression and anxiety symptoms than participants from other age groups.

Anxiety, covid-19, decision tree, depression, logistic regression, tableau, Weka.

ISSN 2637-2835

10.14706/JONSAE2021327

International Burch University

English language

Original research