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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Bibliografische Detailangaben

Hauptverfasser:	Zeyu Wang, Xiaofang Chen, Yiwei Wu, Linke Jiang, Shiming Lin, Gang Qiu
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Nature Portfolio 2025-01-01
Schriftenreihe:	Scientific Reports
Schlagworte:	Healthcare insurance fraud Machine learning Model ensemble Model interpretability
Online-Zugang:	https://doi.org/10.1038/s41598-024-82062-x
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