Diabetes disease prediction system using HNB classifier based on discretization method
Abstract: Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way – through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Diabetes disease prediction system using HNB classifier based on discretization method ; volume:20 ; number:1 ; year:2023 ; extent:13
Journal of integrative bioinformatics ; 20, Heft 1 (2023) (gesamt 13)
- Creator
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Al-Hameli, Bassam Abdo
Alsewari, AbdulRahman A.
Basurra, Shadi S.
Bhogal, Jagdev
Ali, Mohammed A. H.
- DOI
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10.1515/jib-2021-0037
- URN
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urn:nbn:de:101:1-2023033114034328861983
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 11:02 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Al-Hameli, Bassam Abdo
- Alsewari, AbdulRahman A.
- Basurra, Shadi S.
- Bhogal, Jagdev
- Ali, Mohammed A. H.
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