Publication:
Handling imbalanced class problem of measles infectionrisk prediction model

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dc.citedby2
dc.contributor.authorWan Ahmad W.M.T.en_US
dc.contributor.authorGhani N.en_US
dc.contributor.authorDrus S.M.en_US
dc.contributor.authorid57211662334en_US
dc.contributor.authorid56940219600en_US
dc.contributor.authorid56330463900en_US
dc.date.accessioned2023-05-29T07:23:10Z
dc.date.available2023-05-29T07:23:10Z
dc.date.issued2019
dc.description.abstractMeasles is an emerging infectious disease with increasing number of reported cases. It is a vaccine-preventable disease;thus, it is common to have imbalanced class problem in the dataset. This study aims to resolve the imbalanced class problem for the prediction of measles infection risk and to compare the predictive results on a balanced dataset based on three machine learningtechniques. The data that was utilized in this study contained 37,884 records of suspected measles casesthat were highly imbalanced towards negative measles cases. The Synthetic Minority Over-Sampling Technique (SMOTE) was performed to balance thedistribution of the target attribute. The balanced dataset was then modelled using logistic regression, decision tree and Na�ve Bayes. The predicted results indicated that logistic regression executed on the balanced dataset by SMOTE has the highest and most accurateclassification with 94.5% overall accuracy, 93.9% true positive rate, 5.8% false positive rate and 5.1% false negative rate. Therefore, SMOTE and other over-sampling approaches may be applicable to overcome imbalanced class issues in the medical dataset. � BEIESP.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.35940/ijeat.A2649.109119
dc.identifier.epage3435
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85074788142
dc.identifier.spage3431
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85074788142&doi=10.35940%2fijeat.A2649.109119&partnerID=40&md5=8a6c1db2c6e5281263f29c7ebff9d646
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24388
dc.identifier.volume9
dc.publisherBlue Eyes Intelligence Engineering and Sciences Publicationen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleInternational Journal of Engineering and Advanced Technology
dc.titleHandling imbalanced class problem of measles infectionrisk prediction modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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