Cerebrovascular Accident Attack Classification Using Multilayer Feed Forward Artificial Neural Network with Back Propagation Error

Abstract

Problem statement: Most important problems of medical diagnosis. When there is a cerebrovascular accident attach the chances of a successful treatment depends essentially on the early diagnosis. In practice the part of medical errors while diagnosing a stroke type comes to 20-45% even for experienced doctors and the scope of methods of neurovisualization at stroke diagnosis are limited. Approach: In this research study, attempt was made to model the application of Artificial Neural Networks to the classification of patient Cerebrovascular Accident Attack. The Network for the consisted of a three-layer feed forward artificial neural network with back-propagation error method. Results: Data were collected from 100 records of patients at Federal Medical Centre Owo, Nigeria and the Artificial Neural Networks classifier was trained using gradient decent backward propagation algorithm with flexible sigmoid activation function at one hidden layer, with 16 inputs nodes representing stroke onset symptoms at the input layer, 10 nodes at the hidden layer and one node at the output layer representing the type of the attack. Conclusion: The learning Rate γ was set between 0.1 and 0.9 while the epoch set at 150. Initial weight set at Rand (-0.5 and 0.5). The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in short.