Using Machine Learning Technologies to Classify and Predict Heart Disease

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dc.contributor.authorAlrifaie, M.F.
dc.contributor.authorAhmed, Z.H.
dc.contributor.authorHameed, A.S.
dc.contributor.authorMutar, M.L.
dc.date.accessioned2024-09-29T16:16:35Z
dc.date.available2024-09-29T16:16:35Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe techniques of data mining are used widely in the healthcare sector to predict and diagnose various diseases. Diagnosis of heart disease is considered as one of the very important applications of these systems. Data is being collected today in a large amount where people need to rely on the device. In recent years, heart disease has increased excessively and heart disease has become one of the deadliest diseases in many countries. Most data sets often suffer from extreme values that reduce the accuracy percentage in classification. Extreme values are defined in terms of irrelevant or incorrect data, missing values, and the incorrect values of the dataset. Data conversion is another very important way to preconfigure the process of converting data into suitable mining models by acting assembly or assembly and filtering methods such as eliminating duplicate features by using the link and one of the wrap methods, and applying the repeated discrimination feature. This process is performed, dealing with lost values through the "Remove with values" methods and methods of estimating the layer. Classification methods like Naïve Bayes (NB) and Random Forest (RF) are applied to the original datasets and data sets with the feature of selection methods too. All of these operations are implemented on three various sets of heart disease data for the analysis of pre-treatment effect in terms of accuracy. © 2021en_US
dc.identifier.doi10.14569/IJACSA.2021.0120315
dc.identifier.endpage127en_US
dc.identifier.issn2158-107X
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85103719431en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage123en_US
dc.identifier.urihttps://doi.org/10.14569/IJACSA.2021.0120315
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9179
dc.identifier.volume12en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherScience and Information Organizationen_US
dc.relation.ispartofInternational Journal of Advanced Computer Science and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject(Support Vector Machine SVM)en_US
dc.subjectClassificationen_US
dc.subjectmachine learningen_US
dc.subjectNaive Bayes (NB)en_US
dc.subjectRandom Foresten_US
dc.titleUsing Machine Learning Technologies to Classify and Predict Heart Diseaseen_US
dc.typeArticleen_US

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