Due to the technology innovations, a medical diagnosis has developed as an emerging area in the healthcare systems. Over the past decades, different reliable prediction models have been developed according to the survival analysis method with different degree of success. A survival of patient’s after liver transplantation has been predicted by using Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) model for better diagnosis. Conversely, patients undergoing liver transplantation may have a very poor diagnosis. Also, it depends on the proper selection of attributes and model. Hence in this article, an enhanced model is proposed for prediction of long-term survival of patient’s after liver transplantation. Initially, data are collected and the Principal Component Analysis (PCA) is applied for dimensionality reduction which removes unnecessary attributes of liver patients. Then, the data is trained separately by using Convolutional Neural Network (CNN) model with the suitable selection of data attributes. Finally, the performance of the proposed model is analyzed and compared with the existing MLP-ANN model in terms of sensitivity, specificity and accuracy. The experimental results show that the proposed CNN model achieves high prediction accuracy in survival analysis after liver transplantation.

Liver transplantation, Survival prediction, MLP-ANN, Convolutional neural network, Principal component analysis

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