A clinical decision support system based on support vector machine and binary particle swarm optimisation for cardiovascular disease diagnosis. (2016)

• Record Type: Journal Article
• Authors: Sali, Rasoul; Shavandi, Hassan; Sadeghi, Masoumeh
• Is Part Of: International journal of data mining and bioinformatics. Volume 15:Number 4(2016)
• Page Start: 312
• Page End: 327
• Publication Date: 2016
• Subjects: clinical decision making -- cardiovascular disease -- classification -- feature selection -- support vector machines -- SVM -- binary PSO -- particle swarm optimisation -- clinical DSS -- decision support systems -- heart disease diagnosis -- Iran -- bioinformatics; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.