Machine Learning Algorithms To Detect Heart Disease.
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| Title: | Machine Learning Algorithms To Detect Heart Disease. |
|---|---|
| Authors: | Rajesh, P.1 rajesh.palanirajan@gmail.com, Kumar, P. Senthil2 senthilmcasrit@gmail.com |
| Source: | Educational Administration: Theory & Practice. 2024, Vol. 30 Issue 11, p2420-2424. 5p. |
| Abstract: | The growth of the computer science field is very impressive in the current world. The number of diseases that afflict man has increased exponentially, more than his life, due to his current activities. Due to the current practices of human life and activities without sufficient labor, the number of diseases that affect him is increasing, and his life expectancy is also decreasing. The aim of this study is to use the current developments in the field of computer science to try to solve the symptoms of diseases that occur in human life and the methods that help to solve them. Heart-related diseases are the most common cause of death in the world today. To easily detect and resolve these effects, we can use mission learning algorithms in current scientific discoveries to monitor these diseases and try to correct them at an early stage. By using machine learning algorithms, it is possible to easily detect symptoms of the disease and obtain accurate results. [ABSTRACT FROM AUTHOR] |
| Copyright of Educational Administration: Theory & Practice is the property of Educational Administration: Theory & Practice and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 192328081 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine Learning Algorithms To Detect Heart Disease. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rajesh%2C+P%2E%22">Rajesh, P.</searchLink><relatesTo>1</relatesTo><i> rajesh.palanirajan@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+P%2E+Senthil%22">Kumar, P. Senthil</searchLink><relatesTo>2</relatesTo><i> senthilmcasrit@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Educational+Administration%3A+Theory+%26+Practice%22">Educational Administration: Theory & Practice</searchLink>. 2024, Vol. 30 Issue 11, p2420-2424. 5p. – Name: Abstract Label: Abstract Group: Ab Data: The growth of the computer science field is very impressive in the current world. The number of diseases that afflict man has increased exponentially, more than his life, due to his current activities. Due to the current practices of human life and activities without sufficient labor, the number of diseases that affect him is increasing, and his life expectancy is also decreasing. The aim of this study is to use the current developments in the field of computer science to try to solve the symptoms of diseases that occur in human life and the methods that help to solve them. Heart-related diseases are the most common cause of death in the world today. To easily detect and resolve these effects, we can use mission learning algorithms in current scientific discoveries to monitor these diseases and try to correct them at an early stage. By using machine learning algorithms, it is possible to easily detect symptoms of the disease and obtain accurate results. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Educational Administration: Theory & Practice is the property of Educational Administration: Theory & Practice and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=192328081 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.53555/kuey.v30i11.10562 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 5 StartPage: 2420 Titles: – TitleFull: Machine Learning Algorithms To Detect Heart Disease. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rajesh, P. – PersonEntity: Name: NameFull: Kumar, P. Senthil IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 13004832 Numbering: – Type: volume Value: 30 – Type: issue Value: 11 Titles: – TitleFull: Educational Administration: Theory & Practice Type: main |
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