A clinical decision support system based on support vector machine and binary particle swarm optimisation for cardiovascular disease diagnosis Online publication date: Thu, 04-Aug-2016
by Rasoul Sali; Hassan Shavandi; Masoumeh Sadeghi
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 15, No. 4, 2016
Abstract: Cardiovascular diseases have been known as one of the main reasons of mortality all around the world. Nevertheless, this disease is preventable if it can be diagnosed in an early stage. Therefore, it is crucial to develop Clinical Decision Support Systems (CDSSs) that are able to help physicians diagnose the disease and its related risks. This study focuses on cardiovascular disease diagnosis in an Iranian community by developing a CDSS, based on Support Vector Machine (SVM) combined with Binary Particle Swarm Optimisation (BPSO). We used SVM as the classifier and benefited enormously from optimisation capabilities of BPSO in model development as well as feature selection. Finally, experiments were carried out on the proposed system using Isfahan Healthy Heart Program (IHHP) dataset and the performance of the system is compared with other commonly used classification algorithms in term of classification accuracy, sensitivity, specificity and GMean.
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