Paper Title
Fraudulent Health Insurance Claims Detection using Machine Learning
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Abstract
Health insurance fraud harm the integrity and long-term viability of global healthcare systems. With the expanding use of digital technology and electronic health records, there is a greater need for effective fraud detection tools to defend against financial losses and assure the quality of service. This review paper provides a comprehensive summary of current research on detecting false health insurance claims utilising machine learning approaches. This paper discusses a variety of methodology, including supervised and unsupervised learning algorithms, feature engineering techniques, anomaly detection methods, and ensemble learning approaches. It examines the issues of imbalanced datasets, noisy data, and model interpretability, as well as techniques for overcoming them.This research also assesses the effectiveness of machine learning models in detecting false health insurance claims utilizing real-world datasets and performance measurements such as accuracy, precision, recall, and F1 score. We hope that this poll will provide useful insights into the present status of research in this subject, as well as indicate future research directions to improve healthcare fraud detection systems.
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How To Cite (APA)
Harika Gudibandi, G.Mahi Durga Lakshmi, J.Pavaneeth, G.Bhargav Ram, & Ch.Lalitha Syama Sundari (March-2024). Fraudulent Health Insurance Claims Detection using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), d524-d533. https://ijnrd.org/papers/IJNRD2403370.pdf
Issue
Volume 9 Issue 3, March-2024
Pages : d524-d533
Other Publication Details
Paper Reg. ID: IJNRD_215112
Published Paper Id: IJNRD2403370
Downloads: 000122063
Research Area: Engineering
Author Type: Indian Author
Country: Guntur, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2403370.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403370
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