A Machine Learning Framework for Detecting Healthcare Fraud Through Optimized Feature Selection and SMOTE

Abstract

Healthcare fraud remains a major financial burden, requir- ing precise and efficient detection methods. Traditional machine learning (ML) models often face limitations due to redundant features and dataset imbalance, which hinder accuracy. This work presents a ML framework that addresses these issues by incorporating hybrid feature selection and SMOTE-based balancing