Application of Data Mining Algorithms for Feature Selection and Prediction of Diabetic Retinopathy

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dc.contributor.authorOladele, Tinuke Omolewa
dc.contributor.authorOgundokun, R. O.
dc.contributor.authorKayode, A. A.
dc.contributor.authorAdegun, A. A.
dc.contributor.authorAdebiyi, M. O.
dc.date.accessioned2023-05-12T12:47:32Z
dc.date.available2023-05-12T12:47:32Z
dc.date.issued2019
dc.description.abstractDiabetes 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.en_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/10176
dc.language.isoenen_US
dc.publisherComputational Science and Its Applications – ICCSA 2019en_US
dc.subjectData mining, Feature selection, Diabetic retinopathy, Prediction, Classificationen_US
dc.titleApplication of Data Mining Algorithms for Feature Selection and Prediction of Diabetic Retinopathyen_US
dc.typeArticleen_US

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