Publication:
Predicting Sudden Deaths Following Myocardial Infarction in Malaysia Using Machine Learning Classifiers

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dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorHalim M.H.A.en_US
dc.contributor.authorYusoff Y.S.en_US
dc.contributor.authorYusuf M.M.en_US
dc.date.accessioned2024-05-28T08:46:57Z
dc.date.available2024-05-28T08:46:57Z
dc.date.issued2018
dc.description.abstractMyocardial infarction (MI) is among the top causes of death in Malaysia. The mortality rate following MI was high, especially within the first 30 days after the onset. This paper study the ability of k-Nearest Neighbors (kNN) and Naïve Bayes algorithms to predict the 30-day mortality of MI patients, using. The dataset used for this study is provided by National Cardiovascular Disease Database (NCVD) which consist of 2840 MI patients from hospitals in Malaysia. The sudden death predictions made by the machine learning are based on the age, gender, year of onset, smoking habit, BMI, diabetes, hypertension and cholesterol level. The result suggests that kNN algorithm has better performance in predicting the sudden death compared to Naïve Bayes. The number of independent variables plays an important role in mortality prediction, and removing insignificant variables improve the performance.en_US
dc.description.natureFinalen_US
dc.identifier.citationInternational Journal of Engineering & Technology, 7 (4.15) (2018) 4-6en_US
dc.identifier.doi10.14419/ijet.v7i4.15.21360
dc.identifier.epage6
dc.identifier.issn2227524X
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85054630775
dc.identifier.spage4
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85054630775&doi=10.14419%2fijet.v7i4.15.21360&partnerID=40&md5=1b8dbfc6380fed25b7cfba2b17cc81bd
dc.identifier.urihttps://www.sciencepubco.com/index.php/ijet/article/view/21360
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9448
dc.identifier.volume7
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherScience Publishing Corporation Incen_US
dc.relation.ispartofInternational Journal of Engineering and Technology(UAE)en_US
dc.sourceScopus
dc.subjectMachine learningen_US
dc.subjectMortality predictionen_US
dc.subjectMyocardial infarctionen_US
dc.subjectSudden deathen_US
dc.titlePredicting Sudden Deaths Following Myocardial Infarction in Malaysia Using Machine Learning Classifiersen_US
dc.typeArticleen_US
dspace.entity.typePublication

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