Determining the temporal factors of survival associated with brain and nervous system cancer patients: A hybrid machine learning methodology

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dc.authoridDelen, Dursun/0000-0001-8857-5148
dc.authorwosidDelen, Dursun/AGA-9892-2022
dc.contributor.authorNath, Gopal
dc.contributor.authorCoursey, Austin
dc.contributor.authorEkong, Joseph
dc.contributor.authorRastegari, Elham
dc.contributor.authorSengupta, Saptarshi
dc.contributor.authorDag, Asli Z.
dc.contributor.authorDelen, Dursun
dc.date.accessioned2024-05-19T14:46:59Z
dc.date.available2024-05-19T14:46:59Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractPurpose Although different cancer types have been investigated from the perspective of biomedical sciences, machine learning-based studies have been scant. The present study aims to uncover the temporal effects of factors that are important for brain and central nervous system (BCNS) cancer survival, by proposing a machine learning methodology. Methods Several feature selection, data balancing, and machine learning algorithms (in addition to the sensitivity analysis) were employed to analyze the dynamic (i.e. varying) effects of several feature sets on the survival outputs. Results The results show that Gradient Boosting (GB) along with Logistic Regression (LR) and Artificial Neural Networks (ANN) outperform the other classification algorithms in this study. Furthermore, it has been observed that the importance of several features/variables varies from 1- to 5- and 10-year survival predictions. Conclusion Although the proposed hybrid methodology is validated on a large and feature-rich BCNS cancer data set, it can also be utilized to study survival prognostics of other cancer or chronic disease types.en_US
dc.identifier.doi10.1080/20479700.2023.2196101
dc.identifier.issn2047-9700
dc.identifier.issn2047-9719
dc.identifier.scopus2-s2.0-85151487062en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.1080/20479700.2023.2196101
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5630
dc.identifier.wosWOS:000962348400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherRoutledge Journals, Taylor & Francis Ltden_US
dc.relation.ispartofInternational Journal of Healthcare Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectTemporal Effecten_US
dc.subjectMachine Learningen_US
dc.subjectHealthcare Analyticsen_US
dc.subjectParsimonious Modelen_US
dc.subjectBrain And Other Nervous System Canceren_US
dc.subjectPredictive Modelingen_US
dc.titleDetermining the temporal factors of survival associated with brain and nervous system cancer patients: A hybrid machine learning methodologyen_US
dc.typeArticleen_US

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