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dc.contributor.authorNasiru, Muhammad Dankolo
dc.contributor.authorGabi, Danlami
dc.contributor.authorIbrahim, Salisu
dc.date.accessioned2021-05-12T11:17:36Z
dc.date.available2021-05-12T11:17:36Z
dc.date.issued2019-08
dc.identifier.urihttp://hdl.handle.net/11071/11857
dc.descriptionPaper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenyaen_US
dc.description.abstractChronic kidney disease is a general term for heterogeneous disorders affecting kidney structure and function. It is recognized now that even mild abnormalities in measures of kidney structure and function are associated with increased risk for developing complications in other organ systems which lead to mortality, all of which occur more frequently than kidney failure. Data mining has been a current trend for attaining diagnostic results. Huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from massive dataset, categorizing valid and unique patterns in data. In this research we use flower pollination algorithm (FPA) for feature selection method to improve the classification of chronic kidney disease. The experimental result shows that there is a significance improvement in performance of classifiers when FPA feature selection algorithm is applied.en_US
dc.description.sponsorshipKebbi State University of Science and Technology, Aliero, Nigeria Shehu Shagari College of Education Sokoto, Nigeria.en_US
dc.language.isoen_USen_US
dc.publisherStrathmore Universityen_US
dc.subjectFeature Selectionen_US
dc.subjectData Miningen_US
dc.subjectClassificationen_US
dc.titleFlower pollination algorithm feature selection for chronic kidney disease classificationen_US
dc.typeArticleen_US


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  • SIMC 2019 [99]
    5th Strathmore International Mathematics Conference (August 12 – 16, 2019)

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