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dc.contributor.authorDağ, Aslı Z.
dc.contributor.authorAkcam, Zümrüt
dc.contributor.authorKibis, Eyyub
dc.contributor.authorŞimşek, Serhat
dc.contributor.authorDelen, Dursun
dc.date.accessioned2022-03-07T12:49:05Z
dc.date.available2022-03-07T12:49:05Z
dc.date.issued2022en_US
dc.identifier.citationDağ, A. Z., Akcam, Z., Kibis, E., Şimşek, S. ve Delen, D. (2022). A probabilistic data analytics methodology based on Bayesian Belief network for predicting and understanding breast cancer survival. Knowledge-Based Systems, 242(108407). https://doi.org/10.1016/j.knosys.2022.108407en_US
dc.identifier.issn0950-7051
dc.identifier.urihttps://doi.org/10.1016/j.knosys.2022.108407
dc.identifier.urihttps://hdl.handle.net/20.500.12154/1716
dc.description.abstractUnderstanding breast cancer survival has proven to be a challenging problem for practitioners and researchers. Identifying the factors affecting cancer progression, their interrelationships, and their influence on patients’ long-term survival helps make timely treatment decisions. The current study addresses this problem by proposing a Tree-Augmented Bayesian Belief Network (TAN)-based data analytics methodology comprising of four steps: data acquisition and preprocessing, variable selection via Genetic Algorithm (GA), data balancing with synthetic minority over-sampling and random undersampling methods, and finally the development of the TAN model to determine the probabilistic inter-conditional dependency structure among breast cancer-related variables along with the posterior survival probabilities The proposed model is compared to well-known machine learning models. A what-if analysis has also been conducted to verify the associations among the variables in the TAN model. The relative importance of each variable has been investigated via sensitivity analysis. Finally, a decision support tool is developed to further explore the conditional dependency structure among the cancer-related factors. The results produced by the proposed methodology, namely the patientspecific posterior survival probabilities and the conditional relationships among the variables, can be used by healthcare professionals and physicians to improve the decision-making process in planning and managing breast cancer treatments. Our generic methodology can also accommodate other types of cancer and be applied to manage various medical procedures.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBreast Canceren_US
dc.subjectData Miningen_US
dc.subjectGenetic Algorithmen_US
dc.subjectMachine Learningen_US
dc.subjectSensitivity Analysisen_US
dc.titleA probabilistic data analytics methodology based on Bayesian Belief network for predicting and understanding breast cancer survivalen_US
dc.typearticleen_US
dc.departmentİHÜ, Yönetim Bilimleri Fakültesi, İşletme Bölümüen_US
dc.authorid0000-0001-8857-5148en_US
dc.identifier.volume242en_US
dc.ihuauthorid0000-0001-8857-5148en_US
dc.relation.ihupublicationcategory114en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorDelen, Dursun
dc.identifier.doi10.1016/j.knosys.2022.108407en_US
dc.authorscopusid57466556000
dc.authorscopusid57198890763
dc.authorscopusid56604134400
dc.authorscopusid57204002827
dc.authorscopusid55887961100
dc.identifier.wosqualityQ1
dc.identifier.wosWOS:000821048500007
dc.identifier.scopus2-s2.0-85125283691
dc.identifier.scopusqualityQ1en_US


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