Computational methods for predicting the outcome of thoracic transplantation

Abstract Cardiac disease and the death rates due to coronary heart failure cardiomyopathy are increasing. Thoracic transplantation is now a widely accepted therapeutic option for end-stage cardiac failure. The survival rate after organ crucial. Survival prediction hot area of research. use conventional statistical techniques computationally expensive does not provide reliable solutions. Artificial Neural Networks based helps surgeons make precise decisions predict best outcomes. proposed system implements multi-layer perceptron algorithm, which shows good performance in prediction. We also implemented our work Radial Basis Function Network model prove accuracy model. For this research study, data were collected from United Organ Sharing database extracted relevant thoracic attributes with help suitable mining techniques. obtained an 97.1% evaluation various measures. In order assure validity we 92.37%. collated models existing systems proved that appeared be higher compared 85.9% system. outcome will asset lifesaving procedures medical field.