Abstract
The science of preserving and enhancing individual and community health is known as public health. In order to accomplish this task, healthy lifestyle promotion, disease and injury prevention research, and the detection, prevention, and management of infectious diseases are used. Early detection and treatment of health disease can improve the prognosis for the condition worldwide. However, the massive volume of data needed poses a problem to the current automatic algorithms for diagnosing health illness. This study presents a medical gadget that uses the Internet of Things to gather cardiac data from individuals both before and after of heart illness. Technology is developing at a quick pace, leading to the establishment of many methodologies and ongoing research into solutions for problems that arise in a variety of industries. Preprocessing techniques are employed to effectively classify collected health data because the human body generates enormous amounts of data all the time. Furthermore, the most important phase is accurately classifying health data, which is necessary for diagnosis. The Deep Convolutional Neural Network (DCNN) is among the greatest and most efficient methods for categorising medical data. The results of the simulation in experimental research demonstrate that following this advise improves classification accuracy.
Published Version
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