Machine Learning Based Approaches for Healthcare Fraud Detection: A Comparative Analysis

Abstract

Fraudulent activities in healthcare are exponentially increasing and it is the big burden to society. Mitigation of healthcare fraud is one of the most desirable artificial intelligence (AI) services since many organizations experiencing huge benefits which is because of machine learning (ML) systems that spot and prevent fraud in real-time.  Predictive analytics is a branch of data analytics designed at making predictions about future outcomes based on past data and analytics approaches such as machine learning. In this article, a comparative analysis on healthcare fraud detection methods is done by using various machine learning algorithms. It clearly shows that Multilayer Perceptron Algorithm provides significant performance when compared to the other approaches.