Fuzzy-Genetic Methodology for Web-based Computed-Aided Diagnosis in Medical Applications

This paper presents an integrated fuzzy-genetic methodology to address web-based computed-aided diagnosis by using bio-signal processing in medical applications. A deterministic crowding genetic algorithm is used for obtaining different subsets of features that provide high performance classification in a K-Nearest Neighbor classifier. These subsets of features are then used as training data in a rule generator based on fuzzy clustering to obtain a performance qualitative model that can give information about the more suitable features to use in the diagnosis. This model can also be used to assess the (performance) accuracy that will be reached by using a given set of features – possible those ones available at a specific medical centre. The overall methodology is applied to Paroxysmal Atrial Fibrillation (PAF) – the heart arrhythmia that causes more frequently cerebrovascular incidents – Diagnosis based on analysis of nonfibrillation ECGs

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