Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

Background: Coronary artery disease (CAD) is one of the major and important causes of mortality worldwide. The knowledge about the risk factors which increases the probability of developing CAD can help to understand the disease better and also its treatment. Nowadays, many computer-aided approaches have been used for the prediction and diagnosis of diseases. The swarm intelligence algorithms like particle swarm optimization (PSO) have demonstrated great performance in solving different optimization problems. As rule discovery can be modeled as an optimization problem, it can be mapped to an optimization problem and solved by means of an evolutionary algorithm like PSO. Methods: An approach for discovering classification rules of CAD is proposed. The work is based on the real-world CAD dataset and aims at the detection of this disease by producing the accurate and effective rules. An approach based on a hybrid binary-real PSO algorithm is proposed which includes the combination of binary and realvalued encoding of a particle and a different approach for calculating the velocity of particles. The rules were developed from randomly generated particles which take random values in the range of each attribute in the rule. Two different feature selection approaches based on multi-objective evolutionary search and PSO were applied on the dataset and the most relevant features were selected by the algorithms. Results: The accuracy of two different rule sets were evaluated. The rule set with 11 features obtained more accurate results than the rule set with 13 features. Our results show that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD.