Application of Data Mining Algorithms for Feature Selection and Prediction of Diabetic Retinopathy
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
Computational Science and Its Applications – ICCSA 2019
Abstract
Diabetes Retinopathy is a disease which results from a prolonged
case of diabetes mellitus and it is the most common cause of loss of vision in
man. Data mining algorithms are used in medical and computer fields to find
effective ways of forecasting a particular disease. This research was aimed at
determining the effect of using feature selection in predicting Diabetes
Retinopathy. The dataset used for this study was gotten from diabetes retinopathy
Debrecen dataset from the University of California in a form suitable for mining.
Feature selection was executed on diabetes retinopathy data then the Imple mentation of k-Nearest Neighbour, C4.5 decision tree, Multi-layer Perceptron
(MLP) and Support Vector Machines was conducted on diabetes retinopathy data
with and without feature selection. There was access to the algorithms in terms of
accuracy and sensitivity. It is observed from the results that, making use of
feature selection on algorithms increases the accuracy as well as the sensitivity of
the algorithms considered and it is mostly reflected in the support vector machine
algorithm. Making use of feature selection for classification also increases the
time taken for the prediction of diabetes retinopathy.
Description
Keywords
Data mining, Feature selection, Diabetic retinopathy, Prediction, Classification