Key result
A hybrid machine learning model incorporating multiple regressions with RNN and IoT devices successfully predicts pulse and locates coronary disease using accumulated heartbeat data.
Why the study?
Cardiovascular diseases are the primary cause of increasing mortality, but early detection and constant clinical supervision can reduce death rates.
Does a hybrid machine learning model using IoT devices accurately predict heart rate and recognize coronary disease?
Does a hybrid machine learning model using IoT devices accurately predict heart rate and recognize coronary disease?
Answers are grounded in this paper and cite their sources.
A hybrid machine learning model combining multiple regressions with RNN and IoT devices can predict heart rate and assist in recognizing coronary disease.
cardiovascular diseases have been the primary reason for the increasing fatal rate in the recent years. Early detection of heart diseases, as well as constant clinical supervision, can reduce the death rate. To recognize coronary disease using machine learning approaches, a theoretical structure of cloud-based coronary disease evidence and a gauge system was developed. For the specific recognition of the coronary disease, and numerous backslides for the assumption of cardiovascular disease, a useful AI approach has been developed.The proposed method is used to predict respiratory failure by considering various autonomous factors. A hybrid model that incorporates multiple regressions with RNN to predict coronary diseases and IoT devices that use the cloud platform to remind the individual of cardiovascular failure. The final result demonstrates that the proposed learning model essentially predicts the pulse by using accumulated heartbeat data and alternative web of things to locate the coronary disease.
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Manu et al. (2023) studied Cardiovascular disease. Hybrid model incorporating multiple regressions with RNN and IoT devices was evaluated on Prediction of pulse and coronary disease. A hybrid machine learning model incorporating multiple regressions with RNN and IoT devices successfully predicts pulse and locates coronary disease using accumulated heartbeat data.
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