Fig. 2

Average H. Pylori prevalence prediction accuracy and F1- scores of machine learning classifiers using various feature selection methods. Maroon and blue colors represent high and low accuracy (A), and F1 score (B), respectively. The numbers within each cell indicate the accuracy/F1-score of each classifier-feature selection method pair. KNN indicates K-Nearest Neighbors: SVM, Support Vector Machines; XGB, XGBoost; LR, Logistic Regression; NB, Naive Bayes; and RF, Random Forests. FULL indicates all risk factors are used. IG indicates Information Gain: ReF, ReliefF; MRMR, Minimum Redundancy Maximum Relevance; CFS, Correlation-based Feature Selection; FCBF, Fast Correlation Based Filter; and SFFS, Sequential Floating Forward Selection. The numbers -10 and -20 indicate the number of risk factors selected for the ranking-based feature selection methods. C The Receiver Operating Characteristic (ROC) curves of six classifiers (using their best hyperparameter combination) were obtained when they were used to predict H. pylori infection using a subset of risk factors selected through IG-20 feature selection method. The area under the ROC curve (AUROC) for KNN was 0.76, 0.79 for NB, and 0.78 for the other classifiers. The X-axis represents the False Positive Rate (1-Specificity) whereas the Y-axis represents the True Positive Rate (Sensitivity)