Exploring gene expression and clinical data for identifying prostate cancer severity levels using machine learning methods

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dc.authorid0000-0001-6657-9738
dc.contributor.authorMarouf, Ahmed Al
dc.contributor.authorAlhajj, Reda
dc.contributor.authorRokne, Jon G.
dc.contributor.authorGhose, Sunita
dc.contributor.authorBismar, Tarek A.
dc.date.accessioned2023-12-05T06:48:29Z
dc.date.available2023-12-05T06:48:29Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractProstate cancer (PCa) is the most common type of cancer in men worldwide. It is a cancer that starts in the small walnut-shaped male gland called the prostate. From the prostate, it can form a metastasis into other organs. If detected and diagnosed early the survival rate may increase to 95%. Therefore, early detection and diagnosis are important tasks performed by a pathologist. The pathologist identifies the severity levels using a scale called the Gleason grading group (GGG). The GGG is found by pathologists by looking at a biopsy sample and assigning a grade of low, intermediate, or high to the sample. The pathologist then assesses a second sample in the same manner. The GGG is found by adding these two scores provides the total Gleason score. In this paper, we have explored tissue microarray (TMA) and clinical data collected by pathologists of Alberta Precision Laboratory, for predicting the severity level of prostate cancer using various machine learning methods. Traditional classifiers, such as Naïve Bayes, Decision Tree, Support Vector Machine with Radial basis function (RBF), Logistic Regression, and ensemble classifiers, such as Random Forest, and Bagging with k-nearest neighbors have been applied through the machine learning pipeline containing imputation and sampling techniques. An integrated SMOTE-Tomek Links method is adopted for handling the class imbalance problem. The highest accuracy obtained is 99.64% from the Random Forest method.
dc.identifier.citationMarouf, A. A., Alhajj, R., Rokne, J. G., Ghose, S. ve Bismar, T. A. (2023). Exploring gene expression and clinical data for identifying prostate cancer severity levels using machine learning methods. Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) içinde (186-191. ss.). Regina, 24-27 September 2023. https://doi.org/10.1109/CCECE58730.2023.10288946
dc.identifier.doi10.1109/CCECE58730.2023.10288946
dc.identifier.endpage191
dc.identifier.isbn9798350323979
dc.identifier.issn0840-7789
dc.identifier.issue2023-September
dc.identifier.scopus2-s2.0-85177471238
dc.identifier.scopusqualityN/A
dc.identifier.startpage186
dc.identifier.urihttps://doi.org/10.1109/CCECE58730.2023.10288946
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11943
dc.identifier.wos001103161900033en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlhajj, Reda
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofAnnual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGleason Grading Group
dc.subjectMachine Learning
dc.subjectProstate Cancer
dc.subjectRandom Forest
dc.subjectSeverity Levels
dc.titleExploring gene expression and clinical data for identifying prostate cancer severity levels using machine learning methods
dc.typeConference Object

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