M. E. Tabacchi, D. Tegolo, D. Cascio, C. Valenti, S. Sorce, V. Gentile, V. Taormina, I. Brusca, G. Magazz├╣, A. Giuliano, G. Raso

A Fuzzy-Based Clinical Decision Support System for Coeliac Disease

Machine Learning Artificial Intelligence

Coeliac disease (CD) is a permanent inflammatory disease of the small intestine characterized by the destruction of the mucous membrane of this intestinal tract. Coeliac disease represents the most frequent food intolerance and affects about 1% of the population, but it is severely underdiagnosed. Currently available guidelines require CD-specific serology and atrophic histology in duodenal biopsy samples to diagnose CD in adults. In paediatric CD, but recently in adults also, non-invasive diagnostic strategies have become increasingly popular. In order to increase the rates of correct diagnosis of the disease without the use of biopsy, researchers have recently been using approaches based on artificial intelligence techniques. In this work, we present a Clinical Decision Support System (CDSS)system for supporting CD diagnosis, developed in the context of the Italy-Malta cross-border project ITAMA. The implemented CDSS has been based on a neural-network-based fuzzy classifier. The system was developed and tested using a Virtual Database and a Real Database acquired during the ITAMA project. Analysis on 10,000 virtual patients shows that the system achieved an accuracy of 99% and a sensitivity of 99%. On 19,415 real patients, of which 109 with a confirmed diagnosis of coeliac disease, the system achieved 99.6% accuracy, 85.7% sensitivity, 99.6% specificity and 96% precision. Such results show that the developed system can be used effectively to support the diagnosis of the CD by reducing the appeal to invasive techniques such as biopsy.

This article is authored also by Synbrain data scientists and collaborators. READ THE FULL ARTICLE