A Multi-Objective Genetic Algorithm-Based Approach for Explainable Healthcare Fraud Detection

• A MOGA-based framework is proposed for healthcare
fraud detection, which simultaneously optimizes classifi
cation performance and feature subset size.
• The framework integrates LIME to enable instance-level
interpretability, facilitating a deeper understanding of
model predictions.
• A comprehensive comparative analysis is conducted to
evaluate model performance and interpretability before
and after feature selection.