Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients
Fig 2
An overview of the machine learning results.
a) ROC curve for gradient boosting machine (LightGBM), random forest (RF) and their average stacking (Ensemble) using varying prediction probability thresholds from 0 to 1 (step size = 0.01). b) PR curves for the learners using the same thresholding. c) ROC for the final model, DENV5F-AS: 5-Featured Average Stacking of LightGBM and RF, on the test set (AUC = 0.80). d) PR curve for the final model on the test set (PRAUC = 0.69). e) Confusion matrix for DENV5F-AS on the test set (green: all instances, orange: earliest instances of patients), percentages in each direction provide the proportion of instances of each row or column. PL: plasma leakage and noPL: no plasma leakage. The colour intensity is proportional to the ratio of the instances of a matrix cell to the total number of instances. The percentages indicate proportion of classified instances in each cell to the instances in neighbouring cells by row or column, f) Prediction probabilities during the observation period for patients in each class where each point indicate an instance and the connected points indicate that the instances belong to the same patient. The trends of the predicted probabilities for each class are shown by fitting linear regression.