Application of machine learning for the diagnosis of COVID-19

This chapter focuses on the application of machine learning algorithms on the diagnosis of the novel coronavirus disease (COVID-19). First, data visualization is provided on increases in confirmed deaths and recovered cases of COVID-19 using currently available data from Johns Hopkins University. Next, the machine learning algorithms are used for the automatic diagnosis of COVID-19. Data-driven diagnosis is performed using a dataset of 5644 samples with 111 attributes provided by Hospital Israelita Albert Einstein, Brazil. As a preprocessing step, null values and categorical data are processed and standardization is performed. Next, feature selection is performed to find attributes that are most important for a COVID-19 diagnosis. A number of algorithms including random forest logistic regression, XGBoost, and decision tree are considered and their kernel parameters are optimized. The performance of classification algorithms is evaluated in terms of a number of factors including the testing accuracy, precision, recall, miss rate, receiver operating characteristic curve and area under the receiver operating characteristic curve. Experimental results show that serum glucose is the most influential attribute in predicting COVID-19. Our results also show that for the case of cross-validation, XGBoost has the highest accuracy value of 92.67% and logistic regressions have the second highest accuracy of 92.58%, whereas both XGBoost and LR have a 93% value for precision, recall, and F1 score. Moreover, for the case of the holdout method with 20% testing data, logistic regression with an accuracy of 94.06% outperforms other classifiers in terms of accuracy, precision, recall, and F1 score.