Publication:
Chronic Kidney Disease Detection Using Machine Learning Technique
Chronic Kidney Disease Detection Using Machine Learning Technique
| cris.author.scopus-author-id | 57945639800 | |
| cris.author.scopus-author-id | 57945957800 | |
| cris.author.scopus-author-id | 57945878300 | |
| cris.author.scopus-author-id | 56198738900 | |
| cris.author.scopus-author-id | 57219421621 | |
| cris.author.scopus-author-id | 56652165700 | |
| cris.virtual.department | Universiti Malaysia Perlis | |
| cris.virtualsource.department | 9e552ad3-a7b9-4add-93e9-db1efcd610c1 | |
| dc.contributor.author | Al-Momani R. | |
| dc.contributor.author | Al-Mustafa G. | |
| dc.contributor.author | Zeidan R. | |
| dc.contributor.author | Alquran H. | |
| dc.contributor.author | Wan Azani Wan Mustafa | |
| dc.contributor.author | Alkhayyat A. | |
| dc.date.accessioned | 2024-09-28T23:02:12Z | |
| dc.date.available | 2024-09-28T23:02:12Z | |
| dc.date.issued | 2022-01-01 | |
| dc.description.abstract | chronic kidney disease is a disorder that disables normal kidney function. The WHO has shown that CKD is a serious disease, ranked as one of the top twenty causes of death. It is recognized that2 million people worldwide suffer from kidney failure and the number of patients diagnosed with CDK continues to expand at a rate of 5-7% annually. late diagnosis of this disease is a life-threatening problem, which, often occurs in remote areas due to the lack of specialized medical personnel, in addition to the high cost of diagnosis. This paper aims at early detection of CDK using machine learning algorithms Artificial Neural Network, Support Vector Machine, and k-Nearest Neighbor. The importance of AI is reflected in the importance of identifying these typically fatal ailments. This study looks at a data set consisting of 400 samples and 13 features. The three classification techniques were evaluated by applying them to the data. The results show that the ANN classifier achieved the best accuracy at 99.2%. | |
| dc.identifier.doi | 10.1109/IICETA54559.2022.9888564 | |
| dc.identifier.isbn | [9781665472159] | |
| dc.identifier.scopus | 2-s2.0-85140850054 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14170/5878 | |
| dc.language.iso | en | |
| dc.relation.grantno | undefined | |
| dc.relation.ispartof | IICETA 2022 - 5th International Conference on Engineering Technology and its Applications | |
| dc.relation.ispartofseries | IICETA 2022 - 5th International Conference on Engineering Technology and its Applications | |
| dc.subject | Artificial Intelligence | Artificial neural network | Chronic kidney disease | k-nearest neighbor | Kidney disorder detection | Machine Learning | Support vector machine | |
| dc.title | Chronic Kidney Disease Detection Using Machine Learning Technique | |
| dc.type | Conference Proceeding | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 158 | |
| oaire.citation.startPage | 153 | |
| oairecerif.affiliation.orgunit | Jordan University of Science and Technology | |
| oairecerif.affiliation.orgunit | Jordan University of Science and Technology | |
| oairecerif.affiliation.orgunit | Jordan University of Science and Technology | |
| oairecerif.affiliation.orgunit | Jordan University of Science and Technology | |
| oairecerif.affiliation.orgunit | Universiti Malaysia Perlis | |
| oairecerif.affiliation.orgunit | The Islamic University, Najaf | |
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| oairecerif.author.affiliation | Universiti Malaysia Perlis | |
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| person.identifier.scopus-author-id | 57945639800 | |
| person.identifier.scopus-author-id | 57945957800 | |
| person.identifier.scopus-author-id | 57945878300 | |
| person.identifier.scopus-author-id | 56198738900 | |
| person.identifier.scopus-author-id | 57219421621 | |
| person.identifier.scopus-author-id | 56652165700 |
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