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

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Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1109/icaiqsa64000.2024.10882397
Citations
1
Primary Topic
Imbalanced Data Classification Techniques
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article
Field-Weighted Citation Impact
0.35
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Medicare Fraud Detection Using Machine Learning

Sameer Patil, Rahul Suryawanshi, S.G. Chavhan, P. B. Kale et al.
1 citations
Imbalanced Data Classification Techniques
0.35
article

Medicare Fraud Detection Using Machine Learning

Sameer Patil, Rahul Suryawanshi, S.G. Chavhan, P. B. Kale, Swapnil Gaikwad, Ritik Bagde, Vaibhav Piprade
article en
1 citations

Abstract

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

Peace, Justice and strong institutions
Openalex Percentile: Top 29%
Imbalanced Data Classification Techniques
0.35
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