Medicare Fraud Detection Using Machine Learning
The healthcare industry has become an important pillar in modern society but has witnessed increased fraudulent activities. Traditional fraud detection methods have been used to detect potential fraud, but they have been insufficient and time-consuming for certain cases. Data mining, which has emerged as a Fundamental process in knowledge discovery has been successfully applied in Medicare fraud detection. We implemented a prototype that comprised different methods and comparison of each of the methods was carried out to determine which method is most suited for the Medicare dataset
Authors
- Sameer Patil (ORCID: https://orcid.org/0000-0002-8214-0765)
- Rahul Suryawanshi (ORCID: https://orcid.org/0000-0001-6627-4117)
- S.G. Chavhan (ORCID: https://orcid.org/0000-0001-9100-5699)
- P. B. Kale (ORCID: https://orcid.org/0000-0002-2043-2500)
- Swapnil Gaikwad
- Ritik Bagde
- Vaibhav Piprade
Publication Details
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1109/icaiqsa64000.2024.10882397
- Citations
- 1
- Primary Topic
- Imbalanced Data Classification Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.35