A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud
Abstract Healthcare insurance fraud imposes a significant financial burden on healthcare systems worldwide, with annual losses reaching billions of dollars. This study aims to improve fraud detection accuracy using machine learning techniques. Our approach consists of three key stages: data preproce...
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| Principais autores: | , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Assuntos: | |
| Acceso en liña: | https://doi.org/10.1038/s41598-024-82062-x |
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