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A Machine Learning Framework for Detecting Healthcare Fraud Through Optimized Feature Selection and SMOTE
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| dc.contributor.author | Bounab, Rayene | |
| dc.contributor.author | Benzerogue, Sarra; Guelib, Bouchra; ZAROUR , Karim | |
| dc.date.accessioned | 2025-03-17T10:35:56Z | |
| dc.date.available | 2025-03-17T10:35:56Z | |
| dc.date.issued | 25/10/2024 | |
| dc.identifier.uri | http://depot.umc.edu.dz/handle/123456789/14536 | |
| dc.description.abstract | 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 | fr_FR |
| dc.publisher | Université Frères Mentouri - Constantine 1 | |
| dc.title | A Machine Learning Framework for Detecting Healthcare Fraud Through Optimized Feature Selection and SMOTE | fr_FR |
| dc.type | Article | fr_FR |
