Al-Hameli, Bassam Abdo and Al-Sewari, Abdul Rahman Ahmed Mohammed and Basurra, Shadi S. and Bhogal, Jagdev and Ali, Mohammed A H (2023) Diabetes disease prediction system using HNB classifier based on discretization method. Journal of integrative bioinformatics, 20 (1). pp. 1-13. ISSN 1613-4516. (Published)
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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.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Classification; Data mining; Discretization; HNB; Pima dataset |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 20 Jun 2023 06:46 |
| Last Modified: | 20 Jun 2023 06:46 |
| URI: | http://umpir.ump.edu.my/id/eprint/37610 |
| Download Statistic: | View Download Statistics |
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