About: Explainable-Machine-Learning to discover drivers and to predict mental illness during COVID-19

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About: Explainable-Machine-Learning to discover drivers and to predict mental illness during COVID-19 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Explainable-Machine-Learning to discover drivers and to predict mental illness during COVID-19
Creator	Awasthi, Raghav Kumar, Ajit Kumar, Vibhor Prakash Jha, Indra Sethi, Tavpritesh
source	MedRxiv
abstract	COVID-19 pandemic has deeply affected the global economy, education, and travel and stranded people in their respective zones. Most importantly, it has claimed more than half a million lives and is poised to create long and short term consequences through mental health impairment. Identification of important factors affecting mental health can help people manage emotional, psychological, and social well-being. Here, we focus on identifying factors that have a significant impact on mental health during COVID pandemics. In this study, We have used a survey of 17764 adults in the USA at different age groups, genders, and socioeconomic statuses. Through initial statistical analysis followed by Bayesian Network inference, we have identified key factors affecting Mental health during the COVID pandemic. Integrating Bayesian networks with classical machine learning approaches lead to effective modeling of the level of mental health. Overall, females are more stressed than males, and people of age-group 18-29 are more vulnerable to anxiety than other age groups. Using the Bayesian Network Model, we found that people with the chronic medical condition of mental illness are more prone to mental disorders during COVID age. The new realities of working from home, home-schooling, and lack of communication with family/friends/neighbors induces mental pressure. Financial assistance from social security helps in reducing mental stress during COVID generated economic crises. Finally, using supervised ML models, we predicted the most mentally vulnerable people with ~80% accuracy.
has issue date	2020-07-21(xsd:dateTime)
bibo:doi	10.1101/2020.07.19.20157164
has license	medrxiv
sha1sum (hex)	f8af898aad8b17fab1df7e572bedf0a75802d418
schema:url	https://doi.org/10.1101/2020.07.19.20157164
resource representing a document's title	Explainable-Machine-Learning to discover drivers and to predict mental illness during COVID-19
resource representing a document's body	covid:f8af898aad8b17fab1df7e572bedf0a75802d418#body_text
is schema:about of	named entity 'predicted' named entity 'Integrating' named entity 'well-being' named entity 'COVID-19' named entity 'zones' »more»

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