Fig. 2: Evaluation results for the discrimination between COVID-19 positive and COVID-19 negative.
From: Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms
Receiver operating characteristic curves (ROCs) for the discrimination between COVID-19 positive and COVID-19 negative. Performance for symptomatic cases, excluding (a) and including the data after the test date (b), and for no-symptom-reported cases, excluding (c) and including the data after the test date (d), are reported. The model is a gradient boosting prediction model based on decision trees. Median values and 95% confidence intervals (CIs) for sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) are reported, considering the point on the ROC with the highest average value of sensitivity and specificity. Error bars represent 95% CIs. p-values of the one-sided Mann-Whitney U test are reported.
