Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

Abstract

COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.

Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment

“…Due to lack of sufficient number of reverse transcription polymerase chain reaction (RT-PCR) testing kits and expensive cost, it becomes essential to assess the risk levels associated with individuals, so that the individuals at high risk can be given priority for testing. As mentioned in [12], phone-based surveys are analyzed using machine learning algorithms to assess the risk levels associated with individuals into different categories, namely, high risk, moderate risk, and low risk.…”

“…The algorithm assumes that the features are independent of one another. To describe the risk assessment with this algorithm, eight attributes, namely, travel (indicates an individual who had traveled abroad), contact (individual who had been in contact with people who traveled abroad), fever, cough, sneeze, shortness of breath, sore throat, and comorbidity (indicates the presence of chronic illnesses such as heart diseases, diabetes, and lung diseases) are considered [12,36,37].…”

iCovidCare: Intelligent health monitoring framework for COVID-19 using ensemble random forest in edge networks

“…The statistical and correlation methods along with machine learning algorithms in [22] showed better accuracy for predicting the COVID-19 disease severity and mortality rate using several clinical parameters. Moreover, supervised machine learning models for COVID-19 infected patients have been developed in [23] , these learning algorithms validated using epidemiology labeled dataset to identify the positive and negative COVID-19 cases. Also, the correlation coefficient analysis technique has been incorporated to determine the various dependent and independent feature sets before developing the models.…”

Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging

“…In consequence, the foremost deed is to assemble such a technique that could precisely detect COVID-19 during the early stage, in the shortest possible time. There have been many machine learning models that do detect COVID-19 automatically but shortfalls in time check, or even in accurate diagnosis of COVID-19 ( Muhammad, Algehyne, Usman, Ahmad, Chakraborty, Mohammed, 2021 , Müller, Ehlen, Valeske, 2021 , Rasheed, Hameed, Djeddi, Jamil, Al-Turjman, 2021 , Sun, Hong, Song, Li, Wang, 2021 ). As the planet scuffles with COVID-19, every ounce of technical creativity and imagination is deployed to combat this pandemic and bring COVID-19 to an end.…”