COVID-19 detection from signs and symptoms using machine learning

Effective SARS-CoV-2 (COVID-19) screening enables a speedy and precise diagnosis of COVID-19, lowering the load on the health care systems. Different machine learning models were developed for COVID-19 detection. These models are intended to aid physicians globally in the quarantining of patients, especially in poor areas. At the end of 2021, the global officially recorded COVID1-19 cases reached 286,582,541 with 5,430,949 confirmed deaths. This study aimed to deploy and test machine learning-based models for predicting COVID-19 diagnosis. Seven machine learning models have been utilized as Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). The models were trained and tested on data from 2,151,898 tested people, among whom 208,726 (9.7%) were found to have COVID-19. Simply six binary characteristics were used to predict COVID-19 testing results with acceptable accuracy. The data included five early COVID 19 clinical signs and symptoms, such as cough, fever, sore throat, shortness of breath, and headache. Also, the data from people in contact with a confirmed COVID-19 case were used. Overall, we evaluated the possibility of building a model that could be used for prioritizing testing for COVID-19 by asking simple questions depending on the data we acquired from GitHub. When using normal unbalanced data; we achieved 92.9% accuracy, 60.4% sensitivity, 96.4% specificity and 62.2% F-score with the MLP, DT, RF and XGBoost machine learning models. When using balanced classes, all the seven machine learning models produced 91.8% accuracy, 65.1% sensitivity, 94.7% specificity and 60.61% F-score results. When using only symptomatic cases (161.915), MLP, DT, RF and XGBoost models gave 70.8% accuracy, 89.1% sensitivity, 47.3% specificity, and 77.4% F-score results. In the three scenarios XGBoost, RF, DT, and MLP gave the best results. Regarding the training time, XGboost, RF and DT were faster than MLP. Key Words: Machine Learning, Classification, Diagnosis, COVID-19, SARS-COV-2, Signs and Symptoms