Performance of Multi-Layer Feedforward Neural Networks to Predict Liver Transplantation Outcome

Abstract

A novel multisolutional clustering and quantization (MCQ) algorithm has been developed that provides a flexible way to preprocess data. It was tested whether it would impact the neural network's performance favorably and whether the employment of the proposed algorithm would enable neural networks to handle missing data. This was assessed by comparing the performance of neural networks using a well-documented data set to predict outcome following liver transplantation. This new approach to data preprocessing leads to a statistically significant improvement in network performance when compared to simple linear scaling. The obtained results also showed that coding missing data as zeroes in combination with the MCQ algorithm, leads to a significant improvement in neural network performance on a data set containing missing values in 59.4% of cases when compared to replacement of missing values with either series means or medians.