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
