A deep learning-based cardio-vascular disease diagnosis system

Recently ehealth technologies are becoming an overwhelming aspect of public health services that provides seamless access to healthcare information. Machine learning tools associated with IoT technology play important role in developing such technologies. This paper proposes a decision support system-based system (DSS) make diagnosis cardiovascular diseases. It uses deep approaches classify electrocardiogram (ECG) signals. Thus, two-stage long-short term memory (LSTM) based neural network architecture, along adequate preprocessing the ECG signals is designed as diagnosis-aided for cardiac arrhythmia detection on signal analysis. cardio-vascular disease (namely ‘DLCVD’) built meet higher performance requirements terms accuracy, specificity, and sensitivity. must also be capable online real-time classification. Experimental results using Massachusetts Institute Technology-Beth Israel Hospital (MIT-BIH) database show DLCVD led outstanding