Computational methods for predicting the outcome of thoracic transplantation

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