Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques

David Opeoluwa Oyewola,Sanjay Misra,Emmanuel Gbenga Dada,Robertas Damaševičius

doi:10.3390/info12120528

Abstract

The application of machine learning techniques to the epidemiology of COVID-19 is a necessary measure that can be exploited to curtail the further spread of this endemic. Conventional techniques used to determine the epidemiology of COVID-19 are slow and costly, and data are scarce. We investigate the effects of noise filters on the performance of machine learning algorithms on the COVID-19 epidemiology dataset. Noise filter algorithms are used to remove noise from the datasets utilized in this study. We applied nine machine learning techniques to classify the epidemiology of COVID-19, which are bagging, boosting, support vector machine, bidirectional long short-term memory, decision tree, naïve Bayes, k-nearest neighbor, random forest, and multinomial logistic regression. Data from patients who contracted coronavirus disease were collected from the Kaggle database between 23 January 2020 and 24 June 2020. Noisy and filtered data were used in our experiments. As a result of denoising, machine learning models have produced high results for the prediction of COVID-19 cases in South Korea. For isolated cases after performing noise filtering operations, machine learning techniques achieved an accuracy between 98–100%. The results indicate that filtering noise from the dataset can improve the accuracy of COVID-19 case prediction algorithms.

Highlights

On 30 December 2019, the first diagnosis of COVID-19 was first reported at WuhanJinyintan Hospital in a patient with pneumonia of unknown etiology
The dataset used in this research comprises epidemiological data of COVID-19 infection cases in South Korea, which were obtained from the Kaggle database
We present the experimental results of machine learning techniques, such as bagging (BAG), stochastic gradient boosting (BST), bi-directional long short-term memory (BLSTM), support vector machine (SVM), naïve Bayes (NB), random forest (RF), k-nearest neighborhood (KNN), decision tree, and the multinomial logistic regression (LR) for the diagnosis of COVID-19 infection cases

Summary

Introduction

On 30 December 2019, the first diagnosis of COVID-19 was first reported at WuhanJinyintan Hospital in a patient with pneumonia of unknown etiology. The result showed that the virus had a family of coronaviruses called Betacoronavirus 2B [1]. Coronavirus batlike SARS exhibited a close link to the virus of COVID-19. The World Health Organization (WHO) identified the novel coronavirus as extreme acute coronavirus syndrome 2 (SARSCOV-2) and referred to it as coronavirus disorder 2019 (COVID-19) on 30 January 2020 [2]. Fever, headache, chills, myalgia or arthralgia, congested nose, diarrhea, hemoptysis, and conjunctival obstruction are typical symptoms of the disease [3]. This can result in kidney failure, death, and severe acute respiratory syndrome in severe cases of the coronavirus disease [4].

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Conclusion