Key result
IoT-based Raspberry Pi system uses neural networks to remotely monitor vitals and predict CV risk.
Why the study?
Ongoing patient health monitoring in remote locations is important to enable timely medical intervention and reduce the need for in-person doctor visits.
The paper proposes an IoT-based remote health monitoring system integrated with a neural network for predicting heart disease risk.
May reduce in-person visits via remote data access; leaves open need for outcome validation in prospective trials.
Doctors now place a high importance on ongoing patient health monitoring since it gives them the chance to save a patient's life. So, the primary objective is to develop a patient monitoring system that can monitor a patient's various physiological data when they are in a remote location and provide the doctor with this information in real time. The information is made public online so that any doctor in the globe can access it. The necessity for a patient to visit the doctor is lessened via remote patient monitoring. The Raspberry Pi employed here is not only a sensor node but also a CPU, and IOT plays a significant part in this complete system by delivering several apps and services. This data can be sensed, gathered, and published online by an intelligent gadget. The paper suggests a general health monitoring system utilising a neural network-based HDPS (Heart Disease Prediction System). The HDPS system forecasts a patient's risk of developing heart disease. The technique uses medical parameters like sex, blood pressure, age, height, and weight for prediction. as an improvement over the work done in this area up until now.
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Nirbhay Bhati (2023) studied Cardiovascular disease. IoT-based Healthcare Monitoring System was evaluated. An IoT-based healthcare monitoring system utilizing a Raspberry Pi and a Multilayer Perceptron Neural Network was designed to remotely monitor patient vitals and predict cardiovascular disease risk.
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