Future Forecasting of COVID-19: A Supervised Learning Approach.

Mujeeb Ur Rehman,Saeed Rubaiee,Arslan Shafique,Maha Driss,Sohail Khalid

doi:10.3390/s21103322

Abstract

A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy.

Highlights

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In addition to the classifiers mentioned above, we have considered a modified random forest algorithm, known as a three-way Random Forest (TWRF) [33]
For further evaluation of the proposed model, the following parameters are considered. This is a two-dimensional array in which a number of True Positives, True Negatives, False Positives, and False Negatives prediction scores are given for any model

Summary

Introduction

ML can be applied to a range of significant research domains, including natural language processing, healthcare, business applications, intelligent robotic design, gaming, and image processing, among others. ML algorithms work on trial and error-based methodologies. Based on the results from the testing phase, error rate and misclassification are calculated [1]. Several ML algorithms have been used to predict future events in applications such as weather forecasting and disease diagnosis. In the latter case, various classification and regression algorithms, such as Support Vector Machine (SVM) and logistic regression have been used to detect different kinds of diseases [6,7]. Scientists continue to perfect vaccines, but prevention and early diagnosis remain the most effective ways to protect people in the meantime

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Conclusion