Heart disease diagnosis using deep learning and cardiac color doppler ultrasound

Deep learning (DL) has various applications in different fields such as smart agriculture, smart cities, intelligent transportation system, industries, and smart healthcare systems, etc., and it has broken through the current technical bottleneck in different fields. With applications in diverse areas, medical field is one of the most important application of DL in which has been applied extensively. Heart disease is one of the most hazardous lethal diseases, and it must be recognized as early as possible in order to limit the damage caused by this disease. Based on Long short-term memory (LSTM) network and DL technology, a medical diagnosis model is established to carry out heart disease diagnosis. One of the traditional diagnosis methods of heart disease is to obtain the patient's heart structure image by echocardiography. The doctor then gets the information from the image for diagnosis purposes. The main limitation associated with this approach is that it is not time-effective. This study uses LSTM and DL technology to learn data features to diagnose heart disease. The integration of LSTM and DL technology is used to establish a diagnosis model that uses a decision support system for heart disease and to improve the accuracy of disease diagnosis. Further, another goal of this study is to promote the development of DL technology in disease diagnosis frameworks. The results obtained from the experiments revealed that the proposed approach is practical and better than the other approaches for the efficient identification of cardiac disease.