Improving the diagnosis of cardiovascular diseases using a intelligent decision support system with a fog computing approach

The most common types of cardiovascular diseases include congenital heart disease, heart failure, cardiomyopathy, rheumatic heart disease, pulmonary stenosis, and coronary artery disease. The diagnosis of cardiovascular diseases through symptoms is a major challenge in the current global conditions, and if not diagnosed in a timely manner, it can be a cause of death. Due to limited access of cardiac specialists to remote areas, an intelligent decision support system with fog computing approach can be an effective solution to improve the diagnosis of cardiovascular diseases. The aim of this article is to present an intelligent decision support system to enhance the diagnosis of cardiovascular diseases based on fog computing. Initially, medical information and records of 100 patients are collected. Then, the input data need to be cleaned and normalized, and features are extracted, selected, and weighted using feature extraction algorithms. Subsequently, the data is classified using a support vector machine algorithm. The results demonstrate that the accuracy metric in the proposed methods is higher than the baseline article. The proposed memetic-genetic method achieves an accuracy of 93%, while the graph mining-genetic method achieves an accuracy of 91%, compared to the baseline article with an accuracy of 86%. The proposed methods have been able to achieve a higher level of coverage in the ROC curve compared to the baseline article. In the memetic-genetic method, the TPR value is 0.38 at the zero point, while in the graph mining-genetic method, the TPR value is 0.2 at the zero point. In contrast, the baseline article considers a TPR value of zero at the zero point. Furthermore, the memetic-genetic method can reach a TPR value of one faster. The point where it reaches a value of one is 0.4. The proposed methods outperform the baseline article in terms of mean square error, and for the combined memetic-genetic and graph mining-genetic method, they achieve error values of 11% and 15%, respectively.