The Internet of Things (IoT) is utilized to enhance conventional healthcare
organizations in a variety of ways, such as monitoring patient habits. Sensor data from
the IoTs is critical for medical facilities. Due to confidentiality and safety concerns,
data should be safeguarded from unwanted alterations. On the contrary, Blockchain
innovation offers a variety of ways to protect data from alterations. Deep learning
(DL), as a subsection of machine learning (ML), has the revolutionary possibility to
reliably analyze enormous amounts of data at very fast rates, provide insightful
conclusions, and effectively resolve complex problems. Preventive treatment and
prompt intervention for individuals at risk depend heavily on rapid and precise disease
prediction. The capacity to handle consecutive time-series data with recurrent neural
network variations of DL depends on developing prediction systems with improved
accuracy, which is crucial given the increased usage of electronic clinical documents.
Prediction analysis is used for the electronic clinical data kept in the cloud regarding
patient records by the proposed framework, which acquires data from IoT gadgets.
With an accuracy of 98.8 percent, specificity of 98.8 percent, precision of 98.9 percent,
and F-value of 98.8 percent, the bidirectional long short-term memory-based
intelligence medical network for monitoring and precisely predicting heart disease
threat outperforms the current Intelligent cardiovascular illnesses prediction structures.
Keywords: Blockchain, Deep learning, Internet of things, Heart disease, Prediction.
