Advanced Multilayer Perceptron Networks method for Predict the Survival in Liver Transplantation

Abstract

Recently, the computerized medical domain based technologies are improved; liver transplantation (LT) plays a significant handling approach among liver disease patients.  However, in several cases patients may have poor survival rate of prediction when undergoes LT, this is the main concern faced in many scenario. Many scholars had performed various applications of prediction to overcome these challenges. Therefore, the current research is concentrated on designing accurate and effective prediction approach for using advance multilayer perceptron (MLP) technique. The proposed prediction technique is performed on some stages. In the initial stage, the medical data are collected from United Nations Organ Sharing (UNOS) database. From that UNOS, we taken only liver related information. The data are given to the principal component analysis (PCA), it will reduce the attributes dimension for minimizing the complexity. After that, the Advance MLP classifier will predict the attributes into two accurate classes such as, best survival and worst survival with the help of these prediction ranges the patients will definitely get the excellent survival after LT.