A machine learning decision support system for determining the primary factors impacting cancer survival and their temporal effect

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dc.contributor.authorDag, A.Z.
dc.contributor.authorJohnson, M.
dc.contributor.authorKibis, E.
dc.contributor.authorSimsek, S.
dc.contributor.authorCankaya, B.
dc.contributor.authorDelen, D.
dc.date.accessioned2024-05-19T14:33:47Z
dc.date.available2024-05-19T14:33:47Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIt is critical for healthcare providers to accurately determine lung cancer patients' prognostics and develop customized treatment plans. However, lung cancer has proven to be a complex disease, and every patient responds differently to treatment options, making survivability predictions highly challenging. This study proposes a holistic machine learning model that can assist healthcare providers in predicting the temporal effects of lung cancer-related factors on one-, five-, and ten-year survival rates. Variable selection algorithms such as genetic algorithm (GA) and Baruta are employed along with data balancing methods to achieve parsimonious models for survival prediction. Classification results are obtained through logistic regression and extreme gradient boosting algorithms followed by an information fusion technique to combine the classification results and identify the temporal effects of lung cancer variables over time. Results demonstrate that the prediction power of the classification models improved as the survival period increased. The models trained using the GA and intersection variable sets generated better average prediction scores. The study contributes to the cancer literature by analyzing the varying temporal impacts of lung cancer variables over varying time periods. Medical professionals can use these findings to understand better the longitudinal characteristics of lung cancer patients’ survival indicators. © 2023 The Authorsen_US
dc.identifier.doi10.1016/j.health.2023.100263
dc.identifier.issn2772-4425
dc.identifier.scopus2-s2.0-85172268938en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.health.2023.100263
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4335
dc.identifier.volume4en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Inc.en_US
dc.relation.ispartofHealthcare Analyticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectData Balancingen_US
dc.subjectFeature Selectionen_US
dc.subjectLung Canceren_US
dc.subjectMachine Learningen_US
dc.subjectPredictive Analyticsen_US
dc.subjectSurvival Analysisen_US
dc.titleA machine learning decision support system for determining the primary factors impacting cancer survival and their temporal effecten_US
dc.typeArticleen_US

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