Predictions and visualization for confirmed, recovered and deaths COVID-19 cases in Iraq

Abstract

The 2019-2020 coronavirus pandemic is an emerging infectious disease that has been referred to as the "COVID-19", which results from the coronavirus "sars-cov-2" that started in Wuhan, China, in Dec. 2019 and then spread worldwide. In this paper, an attempt for compiling and analyzing the information of the epidemiological outbreaks on "COVID‐19" based upon datasets on "2019‐nCoV" has been presented. An empirical data analysis with the visualizations was conducted for understanding the numbers of the variety of the cases that have been reported (i.e. confirmed, deaths, and recoveries) in and outside of Iraq and carried out a dynamic map visualization of the "Covid-19" expansion in a global manner through the date wise and in Iraq. We an investigation has been carried out as well, which characterized the pandemic effects Iraq and the entire world, with the use of machine learning. A k nearest neighbors' (KNN) model and a linear regression (LR) model have been proposed.This paper included the precise analysis of the confirmed cases, as well as the recovered cases, deaths, predicting the pandemic viral attacks and how far it is expanding in Iraq and the world, the LR model got the highest results, reaching 100 percent.

A missing data imputation method based on salp swarm algorithm for diabetes disease

“…As a result, dealing with concerns relating to lost property necessitates quick and inefficient procedures. This raises the demand for computational and mental resources, such as procedures and theoretical frameworks that can lead to near-completion [6], [7]. Most of the time, inefficient tactics are used since there isn't enough time to identify better ways to cope with lost data at the time of observation, hence ineffective techniques like case deletion are used.…”

“…− Missing data in medical datasets Thinking about how the data points were lost in the first place is the simplest technique to deal with lost data. The three processes of missing data are randomly missing, randomly missing, and unignorable [2], [3], [6], [7]. To begin, the phrase "totally missing completely at random" (MCAR) refers to the fact that the data that is missing is not logged at random.…”