Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients' Recovery.

L J Muhammad,Sani Sharif Usman,Md Milon Islam,Safial Islam Ayon

doi:10.1007/s42979-020-00216-w

L J Muhammad, Sani Sharif Usman + Show 2 more

Open Access

https://doi.org/10.1007/s42979-020-00216-w

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Abstract

Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients’ recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.

Highlights

Severe Acute Respiratory Syndrome Coronavirus two (SARS-CoV-2), the causative agent of novel coronavirus (COVID-19 or 2019-nCoV), has emerged in late 2019 whichThis article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M
The model predicted a minimum of 5 days and a maximum of 35 days as the number days for COVID-19 patients to recover from the pandemic virus
The patients of age between 65–85 years are of high risk not to recover from the COVID-19 pandemic, patients of age between 26–64 years are likely to recover while patients of age between 1–24 years are recovered quickly from COVID19 pandemic

Summary

Introduction

Severe Acute Respiratory Syndrome Coronavirus two (SARS-CoV-2), the causative agent of novel coronavirus (COVID-19 or 2019-nCoV), has emerged in late 2019 whichThis article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Data mining (DM) is an advanced artificial intelligence (AI) technique that is used for discovering novel, useful, and valid hidden patterns or knowledge from dataset [6, 14]. The technique reveals relationships and knowledge or patterns among the dataset in several or single datasets [15, 16] It has widely used for the prognosis and diagnosis of many diseases including Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) that were so far discovered in 2003 and 2012, respectively [16]. As huge dataset generated around the world related to 2019-nCoV pandemic everyday is a treasured resource to be mined and analyzed for useful, valid, and novel knowledge or patterns extraction for better decision-making to contain the outbreak of COVID-19 pandemic. Data mining has been widely applied in many different applications such as predicting patient outcomes, modeling health outcomes, hospital ranking, and evaluation of treatment effectiveness and infection control, stability, and recovery [1, 23, 29]

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