Treatment prediction with machine learning in prostate cancer patients

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dc.contributor.authorAlatas, Emre
dc.contributor.authorKokkulunk, Handan Tanyildizi
dc.contributor.authorTanyildizi, Hilal
dc.contributor.authorAlcin, Goksel
dc.date.accessioned2024-03-13T10:35:24Z
dc.date.available2024-03-13T10:35:24Z
dc.date.issued2023
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractThere are various treatment modalities for prostate cancer, which has a high incidence. In this study, it is aimed to make predictions with machine learning in order to determine the optimal treatment option for prostate cancer patients. The study included 88 male patients diagnosed with prostate cancer. Independent variables were determined as Gleason scores, biopsy, PSA, SUVmax, and age. Prostate cancer treatments, which are dependent variables, were determined as hormone therapy(n = 30), radiotherapy(n = 28) and radiotherapy + hormone therapy(n = 30). Machine learning was carried out in the Python with SVM, RF, DT, ETC and XGBoost. Metrics such as accuracy, ROC curve, and AUC were used to evaluate the performance of multi-class predictions. The model with the highest number of successful predictions was the XGBoost. False negative rates for hormone therapy, radiotherapy, and radiotherapy + hormone therapy treatments were, respectively, 12.5, 33.3, and 0%. The accuracy values were computed as 0.61, 0.83, 0.83, 0.72 and 0.89 for SVM, RF, DT, ETC and XGBoost, respectively. The three features that had the greatest influence on the treatment model prediction for prostate cancer with XGBoost were biopsy, Gleason score (3 + 3), and PSA level, respectively. According to the AUC, ROC and accuracy, it was determined that the XGBoost was the model that made the best estimation of prostate cancer treatment. Among the variables biopsy, Gleason score, and PSA level are identified as key variables in prediction of treatment.en_US
dc.description.sponsorshipAltimath;nbascedil; University Scientific Research Funden_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1080/10255842.2023.2298364
dc.identifier.issn1025-5842
dc.identifier.issn1476-8259
dc.identifier.pmid38148626en_US
dc.identifier.scopus2-s2.0-85180680427
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1080/10255842.2023.2298364
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4413
dc.identifier.wosWOS:001132126100001
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofComputer Methods In Biomechanics And Biomedical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectProstate canceren_US
dc.subjectsupport vector machineen_US
dc.subjectrandom foresten_US
dc.subjectdecision treeen_US
dc.subjectmachine learningen_US
dc.titleTreatment prediction with machine learning in prostate cancer patientsen_US
dc.typeArticleen_US

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