Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak.

ABSTRACT:

Background

Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread.

Methods

In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first

SUBMITTER: Naeem M 

PROVIDER: S-EPMC8725668 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature