Volume 18, No. 6, 2021
New Insight In Heart Disease Prediction Using Soft Computational Technique
Shukla D. , Sahu T. , Kushwaha S. , Jagtap A. , Brajesh R. G.
Abstract
Heart disease is a leading cause of mortality worldwide. The arrhythmia is one of the most common heart disorders. The soft computational techniques, particularly machine learning, have emerged as a powerful tool for predicting heart disorders. In this review article, we explore the recent advances in the use of machine learning for arrhythmia prediction using electrocardiogram (ECG) signals. The available tools include decision trees, support vector machines, random forests, artificial neural networks, and deep learning models. In this review, we present recent studies that have used machine learning for arrhythmia prediction using ECG signals. These studies have shown promising results in terms of accuracy and speed of analysis. Moreover, these techniques can handle uncertainty and imprecision in the ECG signals, which is important for accurate prediction. We have attempted to ensemble the work done by the different researchers in the area of ECG signal processing, analysis and interpretation.
Pages: 8817-8827
Keywords: Artificial intelligent, ECG analysis, CNN, Neural Networks, Genetic algorithms, Feature extraction.