Maxout Neural Network for Big Data Medical Fraud Detection

Abstract

Globally, health care losses due to fraud rise every year, and for this reason fraud detection is an active research area that, in the U.S. alone, can potentially save billions of dollars. We explore the performance of multiple maxout activation variants on the big data medical fraud detection task using neural networks. Maxout networks have gained great success in many computer vision tasks, but there is limited work on other classification tasks. Our experiments compare Rectified Linear Unit, Leaky Rectified Linear Unit, Scaled Exponential Linear Unit, and hyperbolic tangent to four maxout variants. We evaluate the effectiveness of the activation functions on four U.S. Centers for Medicare and Medicaid Services datasets. Throughout this paper, we found that maxout networks are considerably slower to train compared to traditional activation functions. We find that on average, across all datasets, Scaled Exponential Linear Unit’s classification performance is better than any maxout activation, and reported the lowest training time.