FILTER-BASED FEATURE SELECTION TECHNIQUES ON HEART DISEASE PREDICTION

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dc.contributor.authorOA Taye, Akande H. B, OF Bukola, MA Oladiran
dc.date.accessioned2024-04-22T10:14:51Z
dc.date.available2024-04-22T10:14:51Z
dc.date.issued2018-07
dc.description.abstractOne of the predominant pre-processing steps used for development of robust diagnostic model in data mining is feature selection. The technique removes redundant features thereby enhancing the accuracy of computational intelligence. Building a model with irrelevant features may affect the accuracy or result into more memory space usage during the training and classification stage. This paper investigates the influence of filter-based techniques of feature selection on the heart disease diagnosis model. Three filter techniques: Relief-F, Information Gain and Principal Component Analysis were applied on the heart disease dataset. Some selected machine learning algorithms: Artificial Neural Network (ANN), C4.5 Decision Tree, Support Vector Machine (SVM), Naïve Bayes (NB) and K-Nearest Neighhood (KNN) were employed to classify the reduced features. Results from experimental analysis based on accuracy, precision and recall showed the effect of filter techniques on heart disease diagnosis.
dc.identifier.urihttp://aujet.adelekeuniversity.edu.ng/index.php/aujet/article/view/17
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/123456789/12411
dc.language.isoen
dc.publisherAdeleke University Journal of Engineering and Technology
dc.titleFILTER-BASED FEATURE SELECTION TECHNIQUES ON HEART DISEASE PREDICTION
dc.typeArticle

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