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Analysis of Neurooncological Data to Predict Success of operation Through Classification

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dc.contributor.author Bagherzadi, Negin
dc.contributor.author Börcek, Alp Özgün
dc.contributor.author Tokdemir, Gül
dc.contributor.author Çağıltay, Nergiz
dc.contributor.author Maraş, H. Hakan
dc.date.accessioned 2020-04-03T08:32:06Z
dc.date.available 2020-04-03T08:32:06Z
dc.date.issued 2016
dc.identifier.citation Bagherzadi, Negin...et al., "7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)", pp. 485-486, (2016). tr_TR
dc.identifier.uri http://hdl.handle.net/20.500.12416/2882
dc.description.abstract Data mining algorithms have been applied in various fields of medicine to get insights about diagnosis and treatment of certain diseases. This gives rise to more research on personalized medicine as patient data can be utilized to predict outcomes of certain treatment procedures. Accordingly, this study aims to create a model to provide decision support for surgeons in Neurooncology surgery. For this purpose, we have analyzed clinical pathology records of Neurooncology patients through various classification algorithms, namely Support Vector Machine, Multi Perceptron and Naive Bayes methods, and compared their performances with the aim of predicting surgery complication. A large number of factors have been considered to classify and predict percentage of patient's complication in surgery. Some of the factors found to be predictive were age, sex, clinical presentation, previous surgery type etc. For classification models built up using Support Vector Machine, Naive Bayes and Multi Perceptron, Classification trials for Support Vector Machine have shown %77.47 generalization accuracy, which was established by 5-fold cross-validation. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1145/2975167.2985645 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Data Mining tr_TR
dc.subject Support Vector Machine tr_TR
dc.subject Naive Bayes tr_TR
dc.subject Multi Perceptron tr_TR
dc.subject Classifier tr_TR
dc.subject Neuroocology tr_TR
dc.title Analysis of Neurooncological Data to Predict Success of operation Through Classification tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) tr_TR
dc.contributor.authorID 17411 tr_TR
dc.identifier.startpage 485 tr_TR
dc.identifier.endpage 486 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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