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Diagnosis of Prostat Cancer Using Artificial Neural Networks

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dc.contributor.author Sinecen, M
dc.contributor.author Cinar, M
dc.contributor.author Karal, O
dc.contributor.author Engin, M
dc.contributor.author Atesci, YZ
dc.contributor.author Makinaci, M
dc.contributor.author Cakmak, B
dc.date.accessioned 2019-08-19T13:32:08Z
dc.date.available 2019-08-19T13:32:08Z
dc.date.issued 2009
dc.identifier.uri http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/19827
dc.description.abstract Prostat cancer is a disease which is the most common and which is also the second deadly in men. When prostat cancer can be diagnosed ear v, medical surgery operation can be performed and the disease can be treated. I
dc.description.abstract n this study, the aim is to design a classifier based expert system for ear v diagnosis of the organ in constraint phase. The other purpose is to reach informed decision making without biopsy by using following risc factors; PSA (Prostate Spesific Antigen), Free PSA, prostate volume, prostate density, weight, height, BMI (Body Mass Index), smoking and heart-rate. In other words, We want to diagnose cancer in optimum level where decrease the number of patients to whom applied biopsy The other purpose is to investigate a relationship between Body Mass Index and smoking factor and Prostate Cancer.
dc.description.abstract For designed system, different Artificial Neural Networks (ANN) as a classifier were used. Classifiers have the performance Feed Forward with single hidden layer ANN % 84.8 (FF1), Feed forward with two hidden layer AAW %85.8 (FF2), Learning Vector Quantization (LVQ) ANN %71.47 and Radial Basis Function (RBF) ANN % 84. FF2 has the highest permance by %.85.8.
dc.language.iso Turkish
dc.publisher IEEE
dc.title Diagnosis of Prostat Cancer Using Artificial Neural Networks
dc.type Proceedings Paper
dc.identifier.startpage 187
dc.identifier.endpage +
dc.identifier.index WOS

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