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This article is part of the supplement: Computational Intelligence in Bioinformatics and Biostatistics: new trends from the CIBB conference series

Open Access Research

An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications

Antonio d'Acierno1*, Massimo Esposito2 and Giuseppe De Pietro2

Author Affiliations

1 Institute of Food Sciences - National Research Council of Italy, Via Roma 64, Avellino, Italy

2 Institute for High Performance Computing and Networking - National Research Council of Italy, Via P. Castellino 111, Napoli, Italy

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BMC Bioinformatics 2013, 14(Suppl 1):S4  doi:10.1186/1471-2105-14-S1-S4

Published: 14 January 2013

Abstract

Background

The diagnosis of many diseases can be often formulated as a decision problem; uncertainty affects these problems so that many computerized Diagnostic Decision Support Systems (in the following, DDSSs) have been developed to aid the physician in interpreting clinical data and thus to improve the quality of the whole process. Fuzzy logic, a well established attempt at the formalization and mechanization of human capabilities in reasoning and deciding with noisy information, can be profitably used. Recently, we informally proposed a general methodology to automatically build DDSSs on the top of fuzzy knowledge extracted from data.

Methods

We carefully refine and formalize our methodology that includes six stages, where the first three stages work with crisp rules, whereas the last three ones are employed on fuzzy models. Its strength relies on its generality and modularity since it supports the integration of alternative techniques in each of its stages.

Results

The methodology is designed and implemented in the form of a modular and portable software architecture according to a component-based approach. The architecture is deeply described and a summary inspection of the main components in terms of UML diagrams is outlined as well. A first implementation of the architecture has been then realized in Java following the object-oriented paradigm and used to instantiate a DDSS example aimed at accurately diagnosing breast masses as a proof of concept.

Conclusions

The results prove the feasibility of the whole methodology implemented in terms of the architecture proposed.