Fuzzy membership function generation using DMS-PSO for the diagnosis of heart disease

Fuzzy decision support systems (FDDSs) have demonstrated their ability to solve different kinds of problems in various application domains. Currently, there is an increasing interest to generate FDDSs with learning and adaption capabilities. In this paper, DMS-PSO can be merged with FDDS for giving the learning and adaptive capability of FDDSs. Here, DMS-PSO used for optimizing membership functions to design efficient and effective fuzzy DSSs. The proposed model is works as follows: Firstly, the datasets are preprocessed so that important effective attributes are selected to handle noisy data. Secondly, fuzzy rules are learned from example(s) and the optimize membership functions of fuzzy DSSs and adapting the DMS-PSO is done. Finally, to establish the efficiency of the adaptive FDSSs the presentation of the FDDSs is evaluated with quantitative, qualitative and comparative analysis. From the experimental results outcome, adaptive FDSSs obtained better accuracy when compared to the existing systems.