Distinguishing sightings is a commonly used method because of the many applied phenomena that can be analyzed through the method of distinguishing sightings. There are many methods that can be used to classify sightings. Among these methods is the technology of artificial neural networks. In this study, synthetic neural networks were used to classify sightings and find out the most important factors influencing kidney failure. Data was collected from the kidney washing center of the municipality of Al Khums - Libya using a simple random sample. The study included 167 infected and non-kidney failure. Some laboratory analyses have been taken for them, and it has been considered that people with and without kidney failure have a subordinate variant, while the independent variables have been (hemoglobin, blood pressure, potassium, sodium, creatine, Urea, age). The study found that the most significant variable in classification using synthetic neural networks was the urea variable, with an important ratio of 35.0%, followed by the Creatinine variable with an important ratio of 28.2%, while the lowest variable was the gender variable of 2.7%, while a rating accuracy and prediction score (88.2%).............. Keywords:................ Artificial Neural Networks, Kidney Failure, Classification, Prediction, Classification Accuracy, Urea, Creatinine.