To respond to the rapid increase in demand for radiologists in the United States, we perform an exploratory analysis using machine-learning algorithms to identify the presence of pneumonia in chest x-rays. We train a k-Nearest Neighbor (KNN), Convolutional Neural Network (CNN), and RESNET-152-based transfer learning model the the goal of improving healthcare KPI’s such as the average treatment charge and patient wait time while reducing mistakes in treatment by accurately differentiating nominal chest x-rays from those that exhibit pneumonia. We identify the F1 Score as the best metric for this use case, as it balances the recall (the fraction of pneumonia cases we correctly identify) against precision (the fraction of cases identified as pneumonia that are pneumonia). We find that our CNN model performs best, with a validation set F1 Score of 0.964 and a test set score of 0.966. This model has potential for generalized high-impact applications in biomedical imaging and diagnosis.