Fuzzy Neural Networks to Detect Cardiovascular Diseases Hierarchically

For purpose of detecting cardiovascular diseases (CVDs) hierarchically via hemodynamic parameters (HDPs) derived from sphygmogram, a hierarchical fuzzy neural networks (HFNNs) scheme is proposed, which provides a non-invasive way to detect CVDs. To deduce conclusion via HFNNs using HDPs as evidences, method of variance analysis is used to categorize and reduce the dimension of feature space. A unique setting of this work is introducing age factor to adjust fuzzy membership function. Categorized HDPs sets are inhaled at different sub-FNNs of HFNNs according to their importance and necessity, so that HFNNs have higher accuracy, especially in dealing with mixed CVDs. HFNNs gain 10% more accuracy than conventional FNN in discriminating 3 mixed CVDs. The preliminary testing results prove that the proposed method is promising for detecting CVDs.