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

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.

Prediction of heart disease and classifiers’ sensitivity analysis

“…An automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies was presented in [30], their algorithm tested on MIT-BIH database, and the simulation results showed the superiority of their proposed method, especially in predicting minority groups with 90.4 and 100% classification. An approach for discovering classification rules of Coronary artery disease (CAD) was proposed by [31], and it was based on the real-world CAD data set and aims at the detection of this disease by producing the accurate and effective rules, and results showed that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD. An accurate detection of Coronary artery disease (CAD) for Iranian patients was applied in [32] using traditional machine learning algorithms, and to improve the performance of these algorithms, a data preprocessing with normalization was carried out with an accuracy of 93.08% for N2Genetic-nuSVM algorithm.…”

Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals

“…Medical tasks have been modeled over the years by nature-inspired algorithms, especially artificial neural networks and the more recent and popular DL, that brought the much needed support in terms of time efficient diagnosis and clinical feature identification [6][7][8][9][10][11][12][13][14][15][16]. At the other end, data coming from different types of sensors have been also effectively analyzed by neural techniques [17][18][19][20][21][22].…”

Decision Rules Construction: Algorithm Based on EAV Model

“…However, such algorithms often produce a suboptimal solution since they cannot avoid local optima. Popular approximate approaches include greedy algorithms [ 31 , 32 , 33 ] and the whole group of biologically inspired methods, among which the following are worth mentioning: genetic algorithms [ 34 , 35 , 36 ], ant colony optimization algorithms [ 37 , 38 ], swarm-based approaches [ 39 ] and many others as described in [ 40 , 41 ].…”