Abstract
The last several decades have seen cardiovascular illnesses become the leading cause of mortality globally, in both industrialized and developing nations alike. Clinical staff monitoring and early diagnosis of heart disorders can both lower death rates. However, because it takes more intelligence, time, and skill, precise cardiac disease identification in every case and 24-hour patient consultation by a doctor are not yet possible. With the use of machine learning techniques, a preliminary concept for a cloud-based system to predict heart disease has been put out in this study. An effective machine-learning strategy should be applied for the precise identification of cardiac illness. This method was created after a thorough comparison of many machine learning methods in MATLAB coding. The application may thus be utilized by the medical professionals to monitor the patient’s real-time sensor data and begin live video streaming if urgent care is necessary. The ability of the suggested method to notify both parties right away when the patient checks the stage while the doctor isn’t there was a crucial component.
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