Enhancing Medicare Fraud Detection With a CNN-Transformer-XGBoost Framework and Explainable AI

Enhancing Medicare Fraud Detection With a CNN-Transformer-XGBoost Framework and Explainable AI

Healthcare fraud is a critical challenge, contributing significantly to rising healthcare costs and financial losses. This article proposes a hybrid architecture for healthcare fraud detection, combining deep learning-based feature representation with gradient boosting classification and explainable...

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Bibliographic Details
Main Authors: Mohammad Balayet Hossain Sakil, Md Amit Hasan, Md Shahin Alam Mozumder, Md Rokibul Hasan, Shafiul Ajam Opee, M. F. Mridha, Zeyar Aung
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Healthcare fraud detection
hybrid deep learning
convolutional neural networks
transformers
XGBoost
SHAP explainability
Online Access:https://ieeexplore.ieee.org/document/10971341/
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https://ieeexplore.ieee.org/document/10971341/

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