Abstract
The widespread use of smartwatches has increased their specific and complementary activities in the health sector for patient's prognosis. In this study, we propose a framework referred to as smart forecasting CardioWatch (SCW) to measure the heart-rate variation (HRV) for patients with myocardial infarction (MI) who live alone or are outside their homes. In this study, HRV is used as a vital alarming sign for patients with MI. The performance of the proposed framework is measured using machine learning and deep learning techniques, namely, support vector machine, logistic regression, and decision-tree classification techniques. The results indicated that the analysis of heart rate can help health services that are located remotely from the patient to render timely emergency health care. Further, taking more cardiac parameters into account can lead to more accurate results. On the basis of our findings, we recommend the development of health-related software to aid researchers to develop frameworks, such as SCW, for effective provision of emergency health.