Flower pollination algorithm feature selection for chronic kidney disease classification
Date
2019-08
Authors
Nasiru, Muhammad Dankolo
Gabi, Danlami
Ibrahim, Salisu
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Chronic 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.
Description
Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenya
Keywords
Feature Selection, Data Mining, Classification