A probabilistic data analytics methodology based on Bayesian Belief network for predicting and understanding breast cancer survival

Basit Öğe Kaydı
dc.authorid0000-0001-8857-5148
dc.authorscopusid57466556000
dc.authorscopusid57198890763
dc.authorscopusid56604134400
dc.authorscopusid57204002827
dc.authorscopusid55887961100
dc.contributor.authorDelen, Dursun
dc.contributor.authorDelen, Dursun
dc.contributor.authorDağ, Aslı Z.
dc.contributor.authorAkcam, Zümrüt
dc.contributor.authorKibis, Eyyub
dc.contributor.authorŞimşek, Serhat
dc.contributor.authorDelen, Dursun
dc.contributor.otherYönetim Bilimleri Fakültesi, İşletme Bölümü
dc.contributor.otherYönetim Bilimleri Fakültesi, İşletme Bölümü
dc.date.accessioned2022-03-07T12:49:05Z
dc.date.available2022-03-07T12:49:05Z
dc.date.issued2022
dc.departmentİHÜ, Yönetim Bilimleri Fakültesi, İşletme Bölümü
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.
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.2022.108407
dc.identifier.doi10.1016/j.knosys.2022.108407
dc.identifier.issn0950-7051
dc.identifier.scopus2-s2.0-85125283691
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.knosys.2022.108407
dc.identifier.urihttps://hdl.handle.net/20.500.12154/1716
dc.identifier.volume242
dc.identifier.wosWOS:000821048500007
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorDelen, Dursun
dc.institutionauthorid0000-0001-8857-5148
dc.language.isoen
dc.publisherElsevier
dc.relation.ihupublicationcategory114
dc.relation.ispartofKnowledge-Based Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBreast Cancer
dc.subjectData Mining
dc.subjectGenetic Algorithm
dc.subjectMachine Learning
dc.subjectSensitivity Analysis
dc.titleA probabilistic data analytics methodology based on Bayesian Belief network for predicting and understanding breast cancer survival
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublicationde384c43-bcde-4ccb-a0b7-39ead0e59bd0
relation.isAuthorOfPublication.latestForDiscoveryde384c43-bcde-4ccb-a0b7-39ead0e59bd0
relation.isOrgUnitOfPublicationc9253b76-6094-4836-ac99-2fcd5392d68f
relation.isOrgUnitOfPublication.latestForDiscoveryc9253b76-6094-4836-ac99-2fcd5392d68f

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
delen_d.pdf
Boyut:
1.39 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ N/A ]
İsim:
license.txt
Boyut:
1.52 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

İşletme Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu