Fig 1.
Flow diagram of subject selection.
Subjects clinically diagnosed with arboviral (dengue, chikungunya, Zika) infections were recruited from sentinel clinics in Machala, Ecuador. A subset of subjects were selected to test the Severity Index for Suspected Arbovirus (SISA) and Severity Index for Suspected Arbovirus with Laboratory (SISAL) machine learning algorithms.
Table 1.
Overview of demographics, symptoms, and laboratory values for subjects enrolled in the study (n = 543).
Numerical data are shown as means and were analyzed with Welch 2-Sample T-test. Categorical data shown as percentages and analyzed with Fisher’s Exact Test.
Fig 2.
Accuracy (blue), Cohen’s kappa (red), and AUC (green) were calculated for the repeated 10-fold cross validation (left) and the holdout test dataset (right) for prediction of hospitalization status in clinically diagnosed dengue, chikungunya or Zika virus infections. bag = bagged trees, knn = k nearest neighbors, rf = random forest, gbm = generalized boosting models, enet = elastic net, nnet = neural networks, log = logistic regression.
Fig 3.
Calibration plot for SISA prediction.
For the final SISA algorithm (generalized boosting model), the predicted probability of hospitalization is compared to the proportion of actual hospitalization among deciles of subjects in the holdout test set. Perfect predictive ability is represented by the dashed diagonal line.
Fig 4.
Accuracy (blue), Cohen’s kappa (red), and AUC (green) were calculated for the repeated 10-fold cross validation (left) and the holdout test dataset (right) for prediction of hospitalization status in clinically diagnosed dengue, chikungunya or Zika virus infections. bag = bagged trees, knn = k nearest neighbors, rf = random forest, gbm = generalized boosting models, enet = elastic net, nnet = neural networks, log = logistic regression.
Fig 5.
Calibration plot for SISAL prediction.
For the final SISAL algorithm (elastic net regression), the predicted probability of hospitalization is compared to the proportion of actual hospitalization among quintiles of subjects in the holdout test set. Perfect predictive ability is represented by the dashed diagonal line.