Estimation of risk factors associated with colorectal cancer: an application of knowledge discovery in databases

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dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridharputluoglu, hakan/0000-0001-8537-5941
dc.authoridARSLAN, Ahmet Kadir/0000-0001-8626-9542
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidharputluoglu, hakan/ABI-6451-2020
dc.authorwosidARSLAN, Ahmet Kadir/AAA-2409-2020
dc.contributor.authorFirat, Feyza
dc.contributor.authorArslan, Ahmet K.
dc.contributor.authorColak, Cemil
dc.contributor.authorHarputluoglu, Hakan
dc.date.accessioned2024-08-04T20:41:45Z
dc.date.available2024-08-04T20:41:45Z
dc.date.issued2016
dc.departmentİnönü Üniversitesien_US
dc.description.abstractColorectal cancer is one of the first reasons for death due to cancer in the world. The goal of this study is to predict important risk factors of colorectal cancer (CRC) by knowledge discovery in databases (KDD) methods. This study comprised a retrospective CRC data of patients who had been diagnosed with colorectal cancer. The selected records between 1 January 2010 and 1 March 2014 were collected randomly from Turgut Ozal Medical Centre databases. The study included 160 individuals: 80 patients admitted to Department of Oncology and diagnosed with CRC, and 80 control subjects with non-CRC categorization. The groups were matched for age and gender. We mined retrospective CRC data from large integrated health systems with electronic health records. Specific demographical and clinical variables including calcium, hemoglobin, white blood cells, platelets, potassium, sodium, glucose, creatinine and total bilirubin were used in multilayer perceptron (MLP) artificial neural networks (ANN) modeling. In this study, patient and control groups consist of 160 individuals. In each group, 45 of these (56.3%) are male, and 35 (43.7%) are women. Mean age of CRC patients and control groups is 58.6 +/- 13.0. While the accuracy was 71.31% in training dataset (n=122), the accuracy was 81.82% in testing dataset. Area under curve (AUC) values of training and testing datasets were 0.73 and 0.81, respectively. The suggested MLP ANN model identified significant factors of calcium, creatinine, potassium, platelets, sodium, hemoglobin and total bilirubin. Taken together, the suggested MLP ANN model might be used for the estimation of risk factors associated with CRC as an application of medical KDD.en_US
dc.identifier.endpage161en_US
dc.identifier.issn2307-4108
dc.identifier.issn2307-4116
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-84968531318en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage151en_US
dc.identifier.urihttps://hdl.handle.net/11616/97322
dc.identifier.volume43en_US
dc.identifier.wosWOS:000376131300011en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAcademic Publication Councilen_US
dc.relation.ispartofKuwait Journal of Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectcolorectal canceren_US
dc.subjectknowledge discovery in databasesen_US
dc.subjectrisk factorsen_US
dc.titleEstimation of risk factors associated with colorectal cancer: an application of knowledge discovery in databasesen_US
dc.typeArticleen_US

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