Cognitive amalgam with a distributed fuzzy case-based reasoning system for an accurate cardiac arrhythmias diagnosis

Abstract

The distributed case-based reasoning consists of distributing the reasoning through a set of agents and the cases through a set of case bases for improving the performance and for ensuring the maintainability of the reasoning system. The most important challenge of the medical applications resides in the precision of decisions; hence the novel decision support systems should introduce more aspects of reasoning for achieving this criterion. In this paper, we describe a cognitive amalgam with a distributed case-based reasoning enriched by an expert system which infer from an XML knowledge base extracted from the domain expertise. Each cognitive agent capitalises its knowledge from same class cases by using a machine learning algorithm enriched by a fuzzy similarity measures function. The aim of this proposition is to reach the optimal criteria of medical applications. The achieved experimental results applied in the diagnosis of complex type of data obtained from the electrocardiogram ECG signal improves our proposition and promotes more realisations in the medical domain.