Application of Standard Machine Learning Models for Medicare Fraud Detection with Imbalanced Data.
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| Title: | Application of Standard Machine Learning Models for Medicare Fraud Detection with Imbalanced Data. |
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| Authors: | Farahmandazad, Dorsa1 (AUTHOR), Danesh, Kasra2 (AUTHOR), Abadi, Hossein Fazel Najaf3 (AUTHOR) |
| Source: | Risks. Oct2025, Vol. 13 Issue 10, p198. 25p. |
| Database: | Business Source Ultimate |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: bsu DbLabel: Business Source Ultimate An: 188999444 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of Standard Machine Learning Models for Medicare Fraud Detection with Imbalanced Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Farahmandazad%2C+Dorsa%22">Farahmandazad, Dorsa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Danesh%2C+Kasra%22">Danesh, Kasra</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Abadi%2C+Hossein+Fazel+Najaf%22">Abadi, Hossein Fazel Najaf</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Risks%22">Risks</searchLink>. Oct2025, Vol. 13 Issue 10, p198. 25p. |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsu&AN=188999444 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/risks13100198 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 198 Titles: – TitleFull: Application of Standard Machine Learning Models for Medicare Fraud Detection with Imbalanced Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Farahmandazad, Dorsa – PersonEntity: Name: NameFull: Danesh, Kasra – PersonEntity: Name: NameFull: Abadi, Hossein Fazel Najaf IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 22279091 Numbering: – Type: volume Value: 13 – Type: issue Value: 10 Titles: – TitleFull: Risks Type: main |
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