International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
•
Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Reviewer Referral Program
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
WSMCDD-2025
GSMCDD-2025
Conferences Published ↓
RBS:RH-COVID-19 (2023)
ICMRS'23
PIPRDA-2023
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 6 Issue 5
September-October 2024
Indexing Partners
Machine Learning in Medicare Fraud Detection: Safeguarding Public Resources
Author(s) | Ginoop Chennekkattu Markose |
---|---|
Country | United States |
Abstract | Fraud in receipt and provision of Medicare is one of the most dangerous threats to public healthcare delivery systems, wasting billions of dollars annually and distorting the foundations upon which healthcare solutions are based. Conventional approaches to identifying fraud have become ineffective owing to the new and complex techniques undertaken by fraudsters. Through this paper, an effort is made to discuss the role of ML in identifying and combating Medicare fraud, specifically to preserve public assets. Using supervised learning, unsupervised learning, and deep learning are promising methods to detect patterns that are possibly related to fraud activities. Applying these techniques can help analyze a huge amount of data, learn from precedents, and identify elaborate and sophisticated trends that are hardly discernable using traditional approaches. This paper will provide an extensive investigation of various ML approaches to Medicare fraud detection. At this step, we experimentally analyze the most popular and effective ones, like decision trees, random forests, SVM, creative neural networks, and clusters. From the results obtained, it is clear that these advanced ML techniques can enhance the performance of fraud detection methods by dramatically minimizing false positives and enhancing the early detection of fraudsters in claims processing. In addition, the ethical concerns, future prospects, and difficulties of employing ML in this particular field. The use of machine learning in Medicare fraud prevention and identification mechanisms to prevent fraud greatly has the potential to transform the protection of public resources, which is crucial to ensuring that healthcare funds are used optimally. |
Keywords | Machine Learning, Medicare Fraud Detection, Public Resources, Healthcare Fraud, Predictive Modeling, Data Mining, Anomaly Detection |
Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
Published In | Volume 6, Issue 5, September-October 2024 |
Published On | 2024-09-21 |
Cite This | Machine Learning in Medicare Fraud Detection: Safeguarding Public Resources - Ginoop Chennekkattu Markose - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.27682 |
DOI | https://doi.org/10.36948/ijfmr.2024.v06i05.27682 |
Short DOI | https://doi.org/g4qmmw |
Share this
E-ISSN 2582-2160
doi
CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.