Detection of Fraudulent Health Insurance Claims Based on Decision Tree with Principal Component Analysis

Abstract

Fraudulent health insurance claims pose a significant challenge to insurance companies and healthcare providers, leading to substantial financial losses and compromised service quality. In this study, we focused on detecting fraudulent health insurance claims using the decision tree algorithm and principal component analysis (PCA). The objective was to gain valuable insights and extract meaningful patterns from the dataset to enhance fraud detection capabilities. We developed a comprehensive method that employed the decision tree algorithm to build a decision tree-based model and the PCA for dimensionality reduction. By analyzing the data using these algorithms, we were able to capture important patterns and relationships within the dataset. The decision tree algorithm demonstrated reasonable performance, while the PCA exhibited even better results, leveraging the advantage of dimensionality reduction. The findings of this study have significant implications for fraud detection in the healthcare industry. The insights gained from applying the decision tree algorithms and PCA can aid in making informed decisions, identifying trends, and uncovering hidden patterns within the data. Our study recommends implementing advanced fraud detection systems that incorporate these algorithms, continuous monitoring and evaluation, collaboration and data sharing, further research and development, and adherence to regulatory compliance. By following these recommendations, stakeholders in the insurance and healthcare industries can strengthen their fraud detection capabilities, protect their organizations from financial losses, and maintain the integrity of their services. The use of decision tree algorithms and PCA, combined with effective strategies, can significantly contribute to the detection of fraudulent health insurance claims and the overall security and sustainability of the healthcare system.