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

dc.contributor.authorKayode, Aderonke Anthonia
dc.date.accessioned2022-03-08T11:09:55Z
dc.date.available2022-03-08T11:09:55Z
dc.date.issued2019-06
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 Implementation 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.citation14. Oladele TO. Ogundokun, RO, Kayode AA, Adegun AA and Adebiyi MO (2019). Application of Data Mining Algorithms for feature selection and prediction of Diabetic Retinopathy Lecture Notes in Computer Science. Vol. 11623 Springeren_US
dc.identifier.urihttp://dspace.run.edu.ng:8080/jspui/handle/123456789/2041
dc.language.isoenen_US
dc.publisherSpringer Nature Switzerlanden_US
dc.subjectData miningen_US
dc.subjectFeature selectionen_US
dc.subjectDiabetic retinopathyen_US
dc.subjectPredictionen_US
dc.titleApplication of Data Mining Algorithms for Feature Selection and Prediction of Diabetic Retinopathyen_US
dc.typeArticleen_US
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