According to world bank, estimates of the global burden of disease indicate that diseases of the kidney and urinary tract account for approximately 830,000 deaths and 18,467,000 disability-adjusted life years annually, ranking them 12th among causes of death (1.4 percent of all deaths) and 17th among causes of disability (1.0 percent of all disability-adjusted life years) (Appel, 2015). The main factor for the high value is the wrong diagnosis of the urinary system diseases which has related symptoms and this has necessitated the design of a urinary diseases diagnosis system.

The aim of this study is to classify both acute inflammation of the bladder and acute inflammation of the kidneys using Support Vector Machine. The objectives are:       

•  To pre-process the acute inflammation dataset to have normalized data. 
•  To train the network using the pre-processed dataset with Support Vector Machine (SVM).   
•  To evaluate the performance on the network.

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