Long term efficacy of Enalapiril using artificial neural network

Renal failure due to diabetic nephropathy is the main cause of death in patients with insulin dependent diabetes and on average death occurs seven years after the start of persistent albuminuria. Increased arterial blood pressure is an early and common occurrence in diabetic nephropathy. Antihypertensive treatment reduces albuminuria and diminishes the rate of decline in glomerular filtration rate thereby postponing end stage renal failure in patients with diabetic nephropathy. Enalapril is considered for use as a renal protective drug in normotensive diabetics with persistent microalbuminuria. In a short term experimental study, Enalapril has been shown to reduce the albumin exertion rate in adolescent diabetic patients with or without elevated blood pressure and its antiproteinuric effect independent of its effect on systemic blood pressure. Since these tests are quite expensive and time taking, we have formulated this problem in the framework of an artificial neural network (ANN) to measure the long term efficacy of Enalapril. Based upon the data available from testing of patients, the proposed ANN has been trained using a backpropagation (BP) algorithm. The network is used for the long term efficacy of Enalapril phantomly. The proposed network gives encouraging results