Enhancing Medicare Fraud Detection Through Machine Learning: Addressing Class Imbalance With SMOTE-ENN-Reference-Cited by-同舟云学术

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

Published:2024 Issue: Volume:12 Page:54382-54396
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Bounab Rayene¹^ORCID,Zarour Karim¹^ORCID,Guelib Bouchra¹^ORCID,Khlifa Nawres²

Affiliation:

1. LIRE Laboratory, Faculty of New Technologies of Information and Communication, University of Abdelhamid Mehri Constantine 2, Constantine, Algeria

2. Research Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, Tunis, Tunisia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10494514.pdf?arnumber=10494514

Reference52 articles.

1. Combating Fraud In Health Care: An Essential Component Of Any Cost Containment Strategy

2. Explainable machine learning models for Medicare fraud detection

3. Using machine learning for healthcare challenges and opportunities

4. The detection of medicare fraud using machine learning methods with excluded provider labels;Bauder

5. Medicare fraud detection using machine learning methods;Bauder

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Automating Fault Prediction in Software Testing Using Machine Learning Techniques: A Real-World Applications;2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS);2024-07-10

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