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Most people worldwide, irrespective of their age, are suffering from massive cardiac arrest. To detect heart attacks early, many researchers worked on the clinical datasets collected from different open-source datasets like PubMed and UCI repository. However, most of these datasets have collected nearly 13 to 147 raw attributes in textual format and implemented traditional data mining approaches. Traditional machine learning approaches just analyze the data extracted from the images, but the extraction mechanism is inefficient and it requires more number of resources. The authors of this research article proposed a system that is aimed at predicting heart attacks by integrating the techniques of computer vision and deep learning approaches on the heart images collected from the clinical labs, which are publicly available in the KAGGLE repository. The authors collected live images of the heart by scanning the images through IoT sensors. The primary focus is to enhance the quality and quantity of the heart images by passing through two popular components of GAN. GAN introduces noise in the images and tries to replicate the real-time scenarios. Subsequently, the available and newly created images are segmented by applying a multilevel threshold operation to find the region of interest. This step helps the system to predict the accurate attack rate by considering various factors. Earlier researchers have obtained sound accuracy by generating similar heart images and found the ROI parts of the 2D echo images. The proposed methodology has achieved an accuracy of 97.33% and a 90.97% true-positive rate. The reason for selecting the computed tomography (CT-SCAN) images is due to the gray scale images giving more reliable information at a low computational cost.
Reddy et al. (Tue,) studied this question.