A Comparison of some Discriminant Techniques in Predicting Blood Pressure

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dc.contributor.authorYussuf, T
dc.contributor.authorAdeleke, B. L.
dc.contributor.authorOyeyemi, G. M.
dc.contributor.authorAdeleke, M. O.
dc.contributor.authorKareem, A. O.
dc.date.accessioned2023-07-27T09:12:44Z
dc.date.available2023-07-27T09:12:44Z
dc.date.issued2019
dc.description.abstractThe paper compared the performance of the Logistic Regression (LR), Fishers Discriminant Analysis (FDA) and the Support Vector Machines (SVM) in predicting high blood pressure. The variables used in the study are Age, Gender, BMI, Cholesterol and the smoking status. The SVM model was tuned to get the best parameter combination and cost function to avoid over fitting and under fitting of the model. The tuning model was used to compare the performance of the SVM model for sample sizes of 100, 500, 5000 and 7900. This approach was carried out for both the LR and FDA. The Data was divided into train and test data sets in the ratio 80:20 for all sample sizes considered to test the performance of the fitted models. The results from the sample sizes considered showed that for sample size of 100, the FDA performed better than the LR and SVM. But for sample sizes of 500, 5000 and 7900, the SVM performed better than both the LR and LDA. The area under the receiver operating curve showed 81.6% for the test data. This means that about 81.6% of the dataset was correctly predicted. The confusion matrix for the three approaches was computed. The result obtained showed the superiority of SVM to the other two methods.en_US
dc.description.sponsorshipSelf-sponsoreden_US
dc.identifier.citationJournal of Science Researchen_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/11646
dc.language.isoenen_US
dc.publisherFaculty of Science, University of Ibadan, Nigeriaen_US
dc.relation.ispartofseries18;1 - 9
dc.subjectAccuracy, Fisher Discriminant Analysis, Logistic Regression, Support Vector Machines, Performanceen_US
dc.titleA Comparison of some Discriminant Techniques in Predicting Blood Pressureen_US
dc.typeArticleen_US

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