An Unusual Complication of Intracavernous Self-Injection of Papaverine

WOS: A1993LM97000037PubMed ID: 814916

Diagnosis of Prostat Cancer Using Artificial Neural Networks

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. In 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.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

Weekly cases of selected notifiable diseases ( 65 1,000 cases reported during the preceding year), and selected low frequency diseases, United States and U.S. territories, week ending November 3, 2018 (WEEK 44). TABLE 2h, Hepatitis A, B

2018-44-table2H-H.pdfHepatitis (viral, acute), by type: Hepatitis A, Hepatitis B2018744