With the overall increase in the elderly population come additional, necessary medical needs and costs. Medicare is a U.S. healthcare program that provides insurance, primarily to individuals 65 years or older, to offload some of the financial burden associated with medical care. Even so, healthcare costs are high and continue to increase. Fraud is a major contributor to these inflating healthcare expenses. The most common method for undertaking the latter involves manually auditing claims data, which is a time-consuming and expensive process. Machine learning models can greatly cut auditing costs by automatically screening incoming claims and flagging up those that are deemed to be suspicious – i.e., potentially incorrect – for subsequent manual auditing. This work provides a comprehensive study leveraging machine learning methods to detect fraudulent Medicare providers. This work uses publicly available Medicare data and provider exclusions for fraud labels to build and assess three different learners. To lessen the impact of class imbalance, given so few actual fraud labels, this framework employs Logistic Regression creating two class distributions. Results show that the other algorithms have poor performance compared with Logistic Regression. Learners have the best fraud detection performance, particularly for the 80:20 class distributions with average AUC scores, respectively, and low false negative rates. This work successfully demonstrates the efficacy of employing machine learning models to detect Medicare fraud.