Data Science and Intelligent Computing Techniques
A Survey of Heart Disease Prediction using Deep Learning Methods
Authors: Ameet Shah and Dhanpratap Singh
Publishing Date: 05-11-2023
ISBN: 978-81-955020-2-8
Abstract
Heart disease is a prevalent and life-threatening condition that affects a significant portion of the global population. Preventing negative consequences and better health outcomes may be greatly helped by early identification and precise prognosis of cardiac disease. Electrocardiogram (ECG) is the important apparatus which is used for diagnosticians of assessment of cardiogram in the clinical. This will Owing to its ability to learn complicated patterns from massive volumes of data, deep neural networks (DNNs) have recently emerged as strong tools for medical diagnostic and prediction tasks. In this part of research, people investigate the use of deep neural networks for cardiac illness forecasting which is easy way for analysis of cardio problem. We analyse various studies and approaches that have utilized Deep Neural Networks for heart disease prediction. This study gives us whole sheds light on the evolution of methods that use in deep neural networks to forecast cardiac issues. The findings from this survey can guide researchers and practitioners in designing and implementing effective DNN models for heart disease prediction, ultimately contributing to improved clinical decision-making and patient care.In this study, we will examine the various DNN techniques, including CNNs (Convolutional Neural Networks) and Stack Denoising Auto encoder, Deep Belief Network, Recurrent Neural Network (RNN), Long Short term Memory (LSTM) and Gated Recurrent Unit (GRU).
Keywords
Deep Learning, CNN, Stack Denoising Auto-encoders, cardiovascular diseases, ECG
Cite as
Ameet Shah and Dhanpratap Singh, "A Survey of Heart Disease Prediction using Deep Learning Methods", In: Satyasai Jagannath Nanda and Rajendra Prasad Yadav (eds), Data Science and Intelligent Computing Techniques, SCRS, India, 2023, pp. 643-656. https://doi.org/10.56155/978-81-955020-2-8-58