Coronavirus disease (COVID-19) cases analysis using machine-learning applications

Abstract

Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.

RETRACTED ARTICLE: Change in hospital admissions in dental diseases before and during the COVID-19 pandemic

“…Since age and frequency parameter distributions were non-normal (Alhayani and Abdallah 2020 ; Alhayani and llhan 2021 ; Alhayani et al 2021 ), Kruskal–Wallis and Mann–Whitney U tests were used for difference analysis. SPSS 17.0 for windows program was used for statistical analysis at 95% confidence interval with 0.05 significance level (Al-Hayani and Ilhan 2020 ; Kwekha-Rashid et al 2021 ; Hasan and Alhayani 2021 ; Yahya et al 2021 ).…”

Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models

“…From those researches, this comparison table illustrates that the supervised learning plays a vital role in prediction using symptoms, clinical features and chest X-rays. Some researchers used classification model with different algorithms for example, shallow singlelayer perceptron neural network, Gaussian process regression, Bayes Net, logistic, J48, multinomial Naive Bayes classifier, support vector machine etc., [17][18][19][20][21][22][23][24] Some researchers used regression, recurrent neural network and convolutional neural network. From all the experiments and studies, it is noted that supervised learning techniques are alone used to obtain more accurate results.…”

An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning

“…In addition, they considered different parameters of the existing dataset. In past pandemics, researchers have applied traditional mathematical approaches [9] to forecast the spread of disease and mortality rate in a specified period. These traditional methods were effective and gave a better performance in terms of forecasting.…”

“…ML is one of the essential tools for the classification task and is employed to forecast the possible confirmed cases and mortality numbers. Currently, various research has been carried out to forecast the COVID-19 cases using different ML models [9]. From the pool of machine learning (ML) models, we tested various well-known regression and forecasting models such as linear regression [25], polynomial regression [26], and support vector regression [27].…”

“…Therefore, researchers have made predictions and chosen the best ML model based on the available datasets. Various ML models are mentioned in the literature, but the deep learning (DL) models give better performances for forecasting the COVID-19 cases [9]. The DL approach establishes the localized transmissibility (R0) of a virus from a given dataset and grants the prolonged investigation of data to understand the mechanism of COVID-19 transference and the apprehension of susceptible districts and emerging hotspots.…”