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Bayesian-driven Multi-layer Perceptron Applied to Liver Fibrosis Stadialization

This paper proposes the application to the liver fibrosis stadialization of a novel training technique of feed-forward neural networks based on the Bayesian paradigm. Using the Pearson’s r correlation coefficient instead of the standard backpropagation algorithm to update the synaptic weights of a multi-layer perceptron, the proposed model is compared with traditional machine learning algorithms (standard MLP, RBF, PNN, SVM) using a real-life liver fibrosis dataset. The statistical comparison results indicated that the Bayesian-trained MLP proved to be at least as efficient as its classic competitors.