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dc.contributor.advisorAjwad, Rasif
dc.contributor.advisorAshraf, Faisal Bin
dc.contributor.authorSayeed, Sanjana
dc.contributor.authorRashid, Iktisad
dc.contributor.authorSotej, Muktadir Rabbi
dc.date.accessioned2021-12-13T09:02:59Z
dc.date.available2021-12-13T09:02:59Z
dc.date.copyright2021
dc.date.issued2021-01
dc.identifier.otherID 17301189
dc.identifier.otherID 16231004
dc.identifier.otherID 16101113
dc.identifier.urihttp://hdl.handle.net/10361/15734
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 62-65).
dc.description.abstractDengue is a viral disease that spreads in tropical and subtropical regions and is especially prevalent in South-East Asia. To some certain extent, this mosquito-borne disease triggers nationwide epidemics, which results in large number of fatalities. In our study, we mainly worked with two data sets from two countries: Bangladesh and Vietnam. For the Vietnamese data set we have used supervised learning and implemented different prediction models like Decision Tree Classifier, Random Forest, Gradient Boosting, Ada Boosting, XG-Boosting Classifier Model and have taken the best fitted one (that being XG-Boosting Classifier) to predict the severity amongst the dengue infected patients. After predicting severity we analyzed the data set further to identify what might be the possible cause leading towards the DSS or the DHF for the clinical data. In parallel, for the Bangladeshi data set we applied the unsupervised learning technique, Hierarchical Clustering, to find the different clusters of various vital components of the patients according to their blood report. We then analyzed the clusters further to find the severity among the patients, which led them to DSS or DHF.en_US
dc.description.statementofresponsibilitySanjana Sayeed
dc.description.statementofresponsibilityIktisad Rashid
dc.description.statementofresponsibilityMuktadir Rabbi Sotej
dc.format.extent65 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectDengueen_US
dc.subjectDSSen_US
dc.subjectDHFen_US
dc.subjectSuperviseden_US
dc.subjectUnsuperviseden_US
dc.subjectHierarchical clusteringen_US
dc.subjectXg-boostingen_US
dc.subjectClinical dataen_US
dc.subject.lcshMachine Learning
dc.titleAnalysis on dengue severity using machine learning approachen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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