Distributed Time-Sensitive Systems

2Heart Rate Prediction Analysis Using ML and DL: A Review of Existing Models and Future Directions

Rimjhim Gupta¹, Roohi Sille^1* and Tanupriya Choudhury²

¹SOCS, UPES, Dehradun, India

²School of Computer Sciences (SoCS), University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India

Abstract

Slow heart rate is one of the leading causes of mortality from heart disease across the world. Heart health is critical, and it will become even more important to monitor significant changes in heart rate at earlier stages, as this can help identify irregular heart performance. Medical therapy and prognosis can be improved if cardiac problems are detected early. However, the signs of cardiac illness are often vague, making early detection difficult. Machine learning and artificial intelligence approaches are now being researched in the field of healthcare. These strategies have a high potential for automating human jobs and can execute tasks that transcend human capabilities. During the study, we explored several strong data-driven models that comprise auto-regressive integrated moving average (ARIMA) model, random forest regressor, k-nearest neighbor (KNN) regressor, support vector regression (SVR), linear regression, decision-tree (DT) regressor, long short-term memory (LTSM) recurrent neural network algorithm, and other models as this study moves forward. The performance of the models varies from one study to another. For example, models like linear regression, the ...