Earlier Prediction of Cardiovascular Disease Using IoT and Deep Learning Approaches

With the rapid development of technology, the needs for healthcare systems research are increasing rapidly. People around the globe are affected by many diseases. Cardiovascular disease is the major cause of death worldwide. The percentage of premature death due to this disease ranges from 5% in high-income countries and 41% in low-income countries. This shows the importance of proper diagnosis in an early stage. The purpose of this chapter is to implement a medical decision support system for earlier prediction of heart disease with the help of new modern technologies such as Internet of Things (IoT) and deep learning algorithm. This support system enhances medical care and reduces cost. Extracting medical data is progressively becoming more and more necessary for prediction. Here, IoT is used to collect data of heartbeat, body temperature, and breathe rate using various sensors. With the help of data that have been generated, a prediction is done by deep learning algorithm. Deep learning can have one or more hidden layers and its network depth is determined independently. This system acts as a promising tool for proper diagnosis.