Towards Understanding the Psychological Effects of the COVID-19 Pandemic on the Indian Population
20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
; : 1449-1454, 2021.
Article
in English
| Scopus | ID: covidwho-1741210
ABSTRACT
This article explains the preliminary results of the analysis of a public survey carried out in India, assessing the psychological effects on people during the second wave of the COVID-19 pandemic. A survey was designed to categorize the population on the basis of various socio-economic demographics and respondents were then asked to fill out the DASS-21 questionnaire to get their levels of severity of anxiety, depression and stress. The dataset obtained was then further analyzed using various classification machine learning models with the level of severity as the target variable and respondent's attributes as independent variables. A Multinomial Logistic Regression was found to give the best results with an AUC score of 0.94 and was thus, used to predict the severity levels of these three categories, to find various insights from this publicly-sourced dataset. Additionally, the significance of the various socio-demographic attributes asked in the survey was analyzed in order to identify key drivers of mental ailments among the general Indian population. Further, a brief description of segmenting the population using K-Means clustering is provided which attempts to identify population groups that belong to similar socio-economic demographics and suffer from similar mental health issues during the pandemic. Thus, high-risk or high-severity groups can be identified and then could be targeted by the government to provide them relief schemes. This paper applies machine learning on a public dataset to explore the various facets of COVID-induced problems in the Indian Society. © 2021 IEEE.
Clustering; COVID-19 Pandemic; Multinomial Logistic Regression; Population Segmentation; Classification (of information); K-means clustering; Logistic regression; Machine learning; Surveys; Clusterings; Independent variables; Machine learning models; Population segmentations; Psychological effects; Public surveys; Socio-economics; Three categories; Population statistics
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Year:
2021
Document Type:
Article
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