02219nas a2200193   4500000000100000000000100001008004100002260001200043100002900055700001700084700002100101700001600122700001800138245010200156856006300258490000700321520168300328022001402011       2021                            d  c01/20211 aMariam Zomorodi-Moghadam1 aMoloud Abdar1 aZohreh Davarzani1 aXujuan Zhou1 aPawel Plawiak00aHybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease  uhttps://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.124850 v383 a<p>Abstract Coronary artery disease (CAD) is one of the major causes of mortality worldwide. Knowledge about risk factors that increase the probability of developing CAD can help to understand the disease better and assist in its treatment. Recently, modern computer-aided approaches have been used for the prediction and diagnosis of diseases. Swarm intelligence algorithms like particle swarm optimization (PSO) have demonstrated great performance in solving different optimization problems. As rule discovery can be modelled as an optimization problem, it can be mapped to an optimization problem and solved by means of an evolutionary algorithm like PSO. An approach for discovering classification rules of CAD is proposed. The work is based on the real-world CAD data set and aims at the detection of this disease by producing the accurate and effective rules. The proposed algorithm is a hybrid binary-real PSO, which includes the combination of categorical and numerical 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 methods based on multi-objective evolutionary search and PSO were applied on the data set, and the most relevant features were selected by the algorithms. 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.</p>
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