Coronary artery disease detection using computational intelligence methods

Nowadays, cardiovascular diseases are very common and are one of the main causes of death worldwide. One major type of such diseases is the coronary artery disease (CAD). The best and most accurate method for the diagnosis of CAD is angiography, which has significant complications and costs. Researchers are, therefore, seeking novel modalities for CAD diagnosis via data mining methods. To that end, several algorithms and datasets have been developed. However, a few studies have considered the stenosis of each major coronary artery separately. We attempted to achieve a high rate of accuracy in the diagnosis of the stenosis of each major coronary artery. Analytical methods were used to investigate the importance of features on artery stenosis. Further, a proposed classification model was built to predict each artery status in new visitors. To further enhance the models, a proposed feature selection method was employed to select more discriminative feature subsets for each artery. According to the experiments, accuracy rates of 86.14%, 83.17%, and 83.50% were achieved for the diagnosis of the stenosis of the left anterior descending (LAD) artery, left circumflex (LCX) artery and right coronary artery (RCA), respectively. To the best of our knowledge, these are the highest accuracy rates that have been obtained in the literature so far. In addition, a number of rules with high confidence were introduced for deciding whether the arteries were stenotic or not. Also, we applied the proposed method on two challenging datasets and obtained the best accuracy in comparison with other methods.