Designing A Neural Network Model Using K-Means Clusteing For Rısk Analysis Of Lung Cancer Disease

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dc.authorid110138en_US
dc.authorid21327en_US
dc.contributor.authorKaya, Umut
dc.contributor.authorYılmaz, Atınç
dc.contributor.authorŞaykol, Ediz
dc.date.accessioned2020-02-14T11:14:27Z
dc.date.available2020-02-14T11:14:27Z
dc.date.issued2018
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractAccording to the World Health Organization report in 2004, lung cancer belongs to highest mortality rate cancer type compared to others. Genetics and early starting smoke etc. become the basis for lung cancer risk. In recent years, lung cancer cases are increasing with the use of cigarettes at younger ages. One of the most important factor in the treatment of the disease is early diagnosis. Artificial intelligence methods, which have been used in many areas in recent years, are also used for early diagnosis and imaging of diseases. In this study, a hybrid artificial neural network (ANN) model was designed to bring a different perspective to the use of multilayer ANN in the literature for lung cancer risk prediction. Lung cancer risk factors were used as input data in predicting the disease. We tried to estimate the results using clustered data by K-means clustering algorithm and multi-layered NN method. When the results obtained from the normalized and clustered data set are compared with the results in the literature, the proposed model has a higher accuracy value than the other methods.en_US
dc.identifier.citationJAST, 2018;11(2):107-117en_US
dc.identifier.issn2148-1059
dc.language.isoenen_US
dc.publisherHezârfen Aeronautics and Space Technologies Instituteen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectANNen_US
dc.subjectK-means clusteringen_US
dc.subjectLung cancer risk analysisen_US
dc.titleDesigning A Neural Network Model Using K-Means Clusteing For Rısk Analysis Of Lung Cancer Diseaseen_US
dc.typeArticleen_US

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