Diabetes Disease Prediction System using HNB classifier based on Discretization Method

Bassam Abdo, Al-Hameli and Alsewari, AbdulRahman and Basurra, Shadi and Bhogal, Jagdev and Ali, Mohammed (2023) Diabetes Disease Prediction System using HNB classifier based on Discretization Method. Journal of Integrative Bioinformatics. ISSN 1613-4516

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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
Identification Number: https://doi.org/10.1515/jib-2021-0037
22 November 2022Accepted
23 February 2023Published Online
Uncontrolled Keywords: classification, data mining, discretization, HNB, Pima dataset
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-02 - information technology
CAH11 - computing > CAH11-01 - computing > CAH11-01-03 - information systems
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Abdulrahman Alsewari
Date Deposited: 27 Feb 2023 11:17
Last Modified: 27 Feb 2023 11:21
URI: https://www.open-access.bcu.ac.uk/id/eprint/13989

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