Treatment prediction with machine learning in prostate cancer patients

dc.authorid alcin, goksel/0000-0003-2268-9606
dc.authorscopusid 58782287700
dc.authorscopusid 58781634100
dc.authorscopusid 58781847500
dc.authorscopusid 57195608308
dc.authorwosid Alataş, Emre/KFQ-7801-2024
dc.contributor.author Alatas, Emre
dc.contributor.author Kokkulunk, Handan Tanyildizi
dc.contributor.author Tanyildizi, Hilal
dc.contributor.author Alcin, Goksel
dc.date.accessioned 2024-06-23T21:36:54Z
dc.date.available 2024-06-23T21:36:54Z
dc.date.issued 2023
dc.department Kadir Has University en_US
dc.department-temp [Alatas, Emre] Beykent Univ, Fac Econ & Adm Sci, Management Informat Syst, Istanbul, Turkiye; [Alatas, Emre] Kadir Has Univ, Inst Sci & Technol, Management Informat Syst, Istanbul, Turkiye; [Kokkulunk, Handan Tanyildizi] Altinbas Univ, Vocat Sch Hlth Serv, Radiotherapy Program, Istanbul, Turkiye; [Tanyildizi, Hilal] Beykent Univ, Fac Econ & Adm Sci, Int Trade & Finance, Istanbul, Turkiye; [Tanyildizi, Hilal] Istanbul Univ, Inst Social Sci, Business Adm, Istanbul, Turkiye; [Alcin, Goksel] Istanbul Educ & Res Hosp, Dept Nucl Med, Istanbul, Turkiye en_US
dc.description alcin, goksel/0000-0003-2268-9606 en_US
dc.description.abstract There 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.sponsorship Altimath;nbascedil; University Scientific Research Fund en_US
dc.description.sponsorship No Statement Available en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1080/10255842.2023.2298364
dc.identifier.issn 1025-5842
dc.identifier.issn 1476-8259
dc.identifier.pmid 38148626
dc.identifier.scopus 2-s2.0-85180680427
dc.identifier.scopusquality Q3
dc.identifier.uri https://doi.org/10.1080/10255842.2023.2298364
dc.identifier.uri https://hdl.handle.net/20.500.12469/5667
dc.identifier.wos WOS:001132126100001
dc.identifier.wosquality N/A
dc.language.iso en en_US
dc.publisher Taylor & Francis Ltd en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 4
dc.subject Prostate cancer en_US
dc.subject support vector machine en_US
dc.subject random forest en_US
dc.subject decision tree en_US
dc.subject machine learning en_US
dc.title Treatment prediction with machine learning in prostate cancer patients en_US
dc.type Article en_US
dc.wos.citedbyCount 3
dspace.entity.type Publication

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