Enhancing Medicare Fraud Detection Through Machine Learning: Addressing Class Imbalance With SMOTE-ENN

Enhancing Medicare Fraud Detection Through Machine Learning: Addressing Class Imbalance With SMOTE-ENN

The healthcare fraud detection field is constantly evolving and faces significant challenges, particularly when addressing imbalanced data issues. Previous studies mainly focused on traditional machine learning (ML) techniques, often struggling with imbalanced data. This problem arises in various as...

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Détails bibliographiques
Auteurs principaux: Rayene Bounab, Karim Zarour, Bouchra Guelib, Nawres Khlifa
Format: Article
Langue:English
Publié: IEEE 2024-01-01
Collection:IEEE Access
Sujets:
Healthcare fraud
imbalanced data
machine learning (ML)
noisy data
Accès en ligne:https://ieeexplore.ieee.org/document/10494514/

Internet

https://ieeexplore.ieee.org/document/10494514/

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