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Minimizing false negatives of measles prediction model: An experimentation of feature selection based on domain knowledge and random forest classifier

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dc.citedby3
dc.contributor.authorAhmad W.M.T.W.en_US
dc.contributor.authorGhani N.L.A.en_US
dc.contributor.authorDrus S.M.en_US
dc.contributor.authorid55163807800en_US
dc.contributor.authorid56940219600en_US
dc.contributor.authorid56330463900en_US
dc.date.accessioned2023-05-29T07:23:19Z
dc.date.available2023-05-29T07:23:19Z
dc.date.issued2019
dc.description.abstractIn the context of disease prediction model, false negative error occurs when the patient is wrongly predicted as free from the disease.A prediction model development involves the process of data collection and feature selection which extracts relevant features from the dataset. Two commonly employed feature selection approaches are domain knowledge and data-driven, that suffer from bias towards past or current knowledge when applied alone.In this research, we have studied the developmentof measles prediction model by incorporating both the domain knowledge and the data-driven approaches, in particular, the Random Forest classifier.The domain expert has earlier on set the important features based uponhisprior knowledgeon measles for the purpose of minimizing the size of features. Afterward, the attributes became the input in Random Forest classifier and the least important attributes are excluded using the Mean Decrease Gini, in order to experiment its effect on the result. It is found that the removal ofseveral attributes after domain knowledge consultation can provide a good model with less false negative errors. �BEIESP.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.35940/ijeat.A2640.109119
dc.identifier.epage3414
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85074592265
dc.identifier.spage3411
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85074592265&doi=10.35940%2fijeat.A2640.109119&partnerID=40&md5=eb7c6ce92aff7e87ca6d72f9aebdefaa
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24413
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.titleMinimizing false negatives of measles prediction model: An experimentation of feature selection based on domain knowledge and random forest classifieren_US
dc.typeArticleen_US
dspace.entity.typePublication
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