Early diagnosis of Ischemia Stroke using neural network

Abstract

Technological and computing evolution promoted new opportunities to improve the quality of life, in particular, the quality of early detection of acute disease. Many intelligent systems have been developed with the purpose of enhancing health-care and providing better health care facilities at reduced cost. Artificial Intelligent techniques are indeed worth exploring and integrating in the medical system for diagnosis, prediction and prescription. The aim of this paper is to determine a noninvasive method that the general population can easily use to detect whether a patient has cerebral ischemia stroke. The problem addressed in this paper is prediction of possibility of cerebral ischemia and it is estimated from symptoms and risk factors given by the patients. Exactly early prognosis of cerebral ischemia stroke has practical importance in medicine. A feed forward neural network with back propagation was used for decision of cerebral ischemia stroke prediction. Developed Neural network model with appropriate training provides an accuracy of 99.99%.