Key result
Novel SVM-BPSO algorithm improves diagnostic accuracy for cardiovascular disease over standard machine learning models.
Why the study?
Cardiovascular disease is a leading cause of mortality worldwide but is preventable if diagnosed early, necessitating effective clinical decision support systems.
Does a CDSS based on SVM and BPSO improve cardiovascular disease diagnosis compared to other classification algorithms?
Does a CDSS based on SVM and BPSO improve cardiovascular disease diagnosis compared to other classification algorithms?
A novel clinical decision support system using SVM and BPSO was developed for cardiovascular disease diagnosis and evaluated on an Iranian cohort.
May support ML algorithm selection for CVD CDSS; leaves open clinical validation and outcome impact in prospective studies.
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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Sali et al. (2016) studied Cardiovascular disease. Clinical Decision Support System based on SVM and BPSO vs. Other commonly used classification algorithms was evaluated on Classification accuracy, sensitivity, specificity and GMean. A clinical decision support system based on Support Vector Machine and Binary Particle Swarm Optimisation was developed and compared to other algorithms for cardiovascular disease diagnosis.
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