Diabetes disease prediction system using HNB classifier based on discretization method
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
NLM (Medline)
2023
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37610/1/Diabetes%20disease%20prediction%20system%20using%20HNB%20classifier%20based%20on%20discretization%20method.pdf http://umpir.ump.edu.my/id/eprint/37610/ https://doi.org/10.1515/jib-2021-0037 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1831529392136781824 |
|---|---|
| author | Al-Hameli, Bassam Abdo Al-Sewari, Abdul Rahman Ahmed Mohammed Basurra, Shadi S. Bhogal, Jagdev Ali, Mohammed A H |
| author_facet | Al-Hameli, Bassam Abdo Al-Sewari, Abdul Rahman Ahmed Mohammed Basurra, Shadi S. Bhogal, Jagdev Ali, Mohammed A H |
| author_sort | Al-Hameli, Bassam Abdo |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.ump.umpir.37610 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2023 |
| publisher | NLM (Medline) |
| record_format | eprints |
| spelling | my.ump.umpir.376102023-06-20T06:46:16Z http://umpir.ump.edu.my/id/eprint/37610/ Diabetes disease prediction system using HNB classifier based on discretization method Al-Hameli, Bassam Abdo Al-Sewari, Abdul Rahman Ahmed Mohammed Basurra, Shadi S. Bhogal, Jagdev Ali, Mohammed A H QA76 Computer software T Technology (General) 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. NLM (Medline) 2023-03-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/37610/1/Diabetes%20disease%20prediction%20system%20using%20HNB%20classifier%20based%20on%20discretization%20method.pdf 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) https://doi.org/10.1515/jib-2021-0037 https://doi.org/10.1515/jib-2021-0037 |
| spellingShingle | QA76 Computer software T Technology (General) Al-Hameli, Bassam Abdo Al-Sewari, Abdul Rahman Ahmed Mohammed Basurra, Shadi S. Bhogal, Jagdev Ali, Mohammed A H Diabetes disease prediction system using HNB classifier based on discretization method |
| title | Diabetes disease prediction system using HNB classifier based on discretization method |
| title_full | Diabetes disease prediction system using HNB classifier based on discretization method |
| title_fullStr | Diabetes disease prediction system using HNB classifier based on discretization method |
| title_full_unstemmed | Diabetes disease prediction system using HNB classifier based on discretization method |
| title_short | Diabetes disease prediction system using HNB classifier based on discretization method |
| title_sort | diabetes disease prediction system using hnb classifier based on discretization method |
| topic | QA76 Computer software T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/37610/1/Diabetes%20disease%20prediction%20system%20using%20HNB%20classifier%20based%20on%20discretization%20method.pdf http://umpir.ump.edu.my/id/eprint/37610/ https://doi.org/10.1515/jib-2021-0037 https://doi.org/10.1515/jib-2021-0037 |
| url_provider | http://umpir.ump.edu.my/ |