Prediction of Health Issues During Covid-19 using Machine Learning Technique

Abstract

A novel deadly virus named COVID-19 was born in China in 2019. In early 2020, the COVID-19 virus spread worldwide, causing severe infections and deaths due to its infectious characteristics and no medical treatment. It has been termed the most consequential global crisis since the World Wars. The defence involved against COVID-19 spread includes sores like social distancing, personal hygiene, wearing a mask, and using sanitizer. The disaster, affecting billions of lives economically and socially improved its infection impact and has motivated the scientific community to come up with solutions based on computerized digital technologies for diagnosis, prevention, rescue, and estimation of COVID-19. An Artificial Intelligence based analysis made an effort to focus on the available data concerning COVID-19. All of these scientific efforts demand that the data brought to service for analysis should be open source to assist the extension, validation, and collaboration of the work in the fight against the global disaster pandemic. We survey and compare research works in these directions that are accompanied by open-source data and code. We hope that the article will provide the scientific community with an initiative to start open-source-extensible and transparent research in the collective fight against the COVID-19 pandemic. The authors collected data on student spending time on online platforms and facing health issues during the lockdown period from DELHI NCR. The authors gather information primarily from the ‘UCI Repository’“ https://archive.ics.uci.edu/ml/datasets”. The dataset for this study is extracted to predict the number of students who faced health issues during the lockdown period for COVID-19. The authors used logistic regression, 50-50,66-34,80-20 train-test splits and 10-fold cross-validation to analyze the data set.