Comparative performance analysis of machine learning classifiers in detection of childhood pneumonia using chest radiographs

This work extends PneumoCAD, a Computer-Aided Diagnosis system for detecting pneumonia in infants using radiographic images [1], with the aim of improving the system's accuracy and robustness. We implement and compare three contemporary machine learning classifiers, namely: Naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machines (SVM). Results of our experiments demonstrate that the SVM classifier produces the best overall results.