Intelligent decision-making for liver fibrosis stadialization based on tandem feature selection and evolutionary-driven neural network

Hepatic fibrosis represents the principal pointer to the development of liver diseases. The correct evaluation of its degree, based on both recent non-invasive procedures and machine learning models, is of current major concern. One of the latest medical imaging methodologies for assessing it is the Fibroscan, supported by biochemical and clinical examinations. Since the complex interaction between the Fibroscan stiffness indicator and the biochemical and clinical results is hard to be manually managed towards the liver fibrosis stadialization, well-performing machine learning algorithms have been proposed to support an automatic diagnosis. We propose in this paper a tandem feature selection mechanism and evolutionary-driven neural network as a computer-based support for liver fibrosis stadialization in chronic hepatitis C. A synergetic system, based on both specific statistical tools and the sensitivity analysis provided by neural networks is used for reducing the dimension of the database from twenty-five to just six attributes. An evolutionary-trained neural network is developed afterwards for the classification of the liver fibrosis stages. The tandem approach is direct and simple, resulting from embedding the feature selection system into the method structure, in order to dynamically concentrate the search only on the most relevant attributes. Experimental results and a thorough statistical analysis clearly demonstrated the efficiency of the proposed intelligent system in comparison with other machine learning techniques reported in literature.

► A tandem feature selection mechanism and evolutionary-trained neural network model is proposed. ► The current paper investigates the tandem decision system performance in terms of accuracy, simplicity and usefulness. ► Its decision performance has been validated on a benchmark real-world dataset regarding liver fibrosis.