Using Two-Layered Feed-Forward Neural Networks to Model Blood Uric Acid Among Diabetic Patients.

Introduction: The end product of purine metabolism in humans is uric acid (UA). Although uric acid can function as either an antioxidant or an oxidant depending on the surrounding environment, the chemical environment can also impact uric acid.The uric acid level in the serum can predict the development of diabetic nephropathy in type 1 diabetes. Objective: This study aims to determine factors that are perhaps having an association with acid uric. Method: Variables selection is basedon clinical importance. The most significant variable will be assigned and analyzed using Artificial Neural Network (ANN) through multilayer feed-forward and contour plot. Results: Through the architecture of MLFF with two hidden layers, it was found that Creatinine level, Urea level, Systolic Blood Pressure reading, Waist circumference reading, Gender play an essential role toward uric level with an accuracy of 97.7% and the predicted mean squared error (MSE.net) is 0.005. The combination of the selected variable showing the highest significance in predicting the level of uric acid. Conclusion: These findings offer useful future management action plans for patients with diabetes.By controlling these four variables can improve the level of health among diabetic patients. [ABSTRACT FROM AUTHOR]