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Predictive modeling for dengue patient�s length of stay (LoS) using big data analytics (BDA)

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dc.citedby1
dc.contributor.authorHendri H.J.M.en_US
dc.contributor.authorSulaiman H.en_US
dc.contributor.authorid57218831669en_US
dc.contributor.authorid54903312800en_US
dc.date.accessioned2023-05-29T06:53:18Z
dc.date.available2023-05-29T06:53:18Z
dc.date.issued2018
dc.descriptionBig data; Data Analytics; Decision support systems; Health care; Demographic data; Descriptive analysis; Healthcare facility; Hospital costs; Length of stay; Modeling technique; Predictive modeling; Resource planning; Predictive analyticsen_US
dc.description.abstractBig data analytics (BDA) in healthcare has become increasingly popular as it offers numerous benefits healthcare stakeholders including physicians, management and insurers. By using dengue epidemic as a case, we identified patient�s length of stay (LoS) as a parameter for the efficiency of care and potentially optimize hospital costs. This paper reports findings from two healthcare facilities based in Malaysia, which recorded 9,261 dengue patients in the year 2014. The main purpose of this study is to provide descriptive analysis and propose big data analytics modeling technique to determine and predict LoS of dengue patients. Demographic data such as age, gender, admission and discharge date have been identified as factors that contribute to the prediction of LoS. The suggested predictive modeling technique may improve resource planning through the use of simple decision support system. Recommendations of this study may also assist the expectation of healthcare facilities on their patient�s LoS. � Springer International Publishing AG 2018.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-319-59427-9_2
dc.identifier.epage19
dc.identifier.scopus2-s2.0-85090369863
dc.identifier.spage12
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369863&doi=10.1007%2f978-3-319-59427-9_2&partnerID=40&md5=f983388cb0df33c33f715ddf0c1eed40
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23937
dc.identifier.volume5
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes on Data Engineering and Communications Technologies
dc.titlePredictive modeling for dengue patient�s length of stay (LoS) using big data analytics (BDA)en_US
dc.typeBook Chapteren_US
dspace.entity.typePublication
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