Diagnosis of Prostat Cancer using Artificial Neural Networks

Prostate cancer is a disease which is the most common and which is also the second deadly in men. When prostate cancer can be diagnosed early, medical surgery operation can be performed and the disease can be treated. In this study, the aim is to design a classifier based expert system for early diagnosis of the organ in constraint phase. The other purpose is to reach informed decision making without biopsy by using following rise factors; PSA (prostate specific 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. 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 ANN %85.8 (FF2), learning vector quantization (LVQ) ANN %71.47 and radial basis function (RBF) ANN % 84. FF2 has the highest performance by %85.8.