INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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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"Fraudulent Health Insurance Claims Detection using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.d524-d533, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403370.pdf
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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