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

I. Dvorchik; M. Subotin; W. Marsh; J. McMichael; J. J. Fung

doi:10.1055/s-0038-1634637

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 1996; 35(01): 12-18
DOI: 10.1055/s-0038-1634637

Original Article

Schattauer GmbH

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

Autor*innen

I. Dvorchik

¹Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh PA, USA
M. Subotin

¹Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh PA, USA
W. Marsh

¹Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh PA, USA
J. McMichael

¹Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh PA, USA
J. J. Fung

¹Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh PA, USA

Abstract

A novel multisolutional clustering and quantization (MCO) 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 MCO 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.

Keywords

Neural Network - Algorithms - Cluster Analysis - Missing Data - Liver Transplantation

Volltext

Referenzen

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