Is There Any Advantage of Machine Learning to Multivariate Regression Analysis for Predicting Disease-Related Deaths in Patients with Gastric Cancer? Reevaluation of Retrospective Data

Basit Öğe Kaydı
dc.authorid110138en_US
dc.authorid227240en_US
dc.authorid259286en_US
dc.authorid148134en_US
dc.contributor.authorYılmaz, Atınç
dc.contributor.authorKaya, Umut
dc.contributor.authorYaprak, Gökhan
dc.contributor.authorÖzen, Alaattin
dc.date.accessioned2021-09-03T12:06:06Z
dc.date.available2021-09-03T12:06:06Z
dc.date.issued2021
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractOBJECTIVE The problem in gastric cancer patients is multifactorial and it is not possible to use one factor alone to predict disease survival. Machine learning (ML) algorithms have become popular in the medical field, recently. We aimed to evaluate the power of ML algorithms for predicting deaths due to gastric cancer. METHODS We reevaluated the retrospective data published. Seven different ML algorithms (logistic regression [LR], artificial neural networks/multilayer perceptron, gradient boosted trees, support vector machine, random forest, naive Bayes, and probabilistic neural network) tried to predict disease-related deaths using the significant variables effective on disease-specific survival (DSS) obtained from univariate analysis. RESULTS Median follow-up time was 34 months (4-156 months), and the death with disease occurred in 194 (86.6%) patients in the follow-up period. The median DSS was 22 (4-139) months. Using the significant variables effective on DSS obtained from univariate analysis, the highest accuracy rate (99%) was the best in the LR, and only one patient was classified incorrectly. CONCLUSION We can successfully predict the treatment outcomes such as disease-related deaths in gastric cancer patients using ML algorithms.en_US
dc.identifier.citationTurk J Oncol 2021;36(2):184–90en_US
dc.identifier.doi10.5505/tjo.2021.2566
dc.identifier.issn1972-2680
dc.identifier.scopus2-s2.0-85108616806en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.trdizinid514120en_US
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/514120
dc.identifier.urihttps://doi.org/10.5505/tjo.2021.2566
dc.identifier.wosWOS:000658350800007en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherKARE PUBLen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectDisease-related deathen_US
dc.subjectGastric canceren_US
dc.subjectMachine learning algortihmsen_US
dc.titleIs There Any Advantage of Machine Learning to Multivariate Regression Analysis for Predicting Disease-Related Deaths in Patients with Gastric Cancer? Reevaluation of Retrospective Dataen_US
dc.typeArticleen_US

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