Abstract
According to the WHO (World Health Organization) chronic diseases such as cancer, coronary heart disease, diabetes mellitus type 2, and chronic obstructive pulmonary diseases are among the world's most common diseases constitute because of this about 60% of all deaths occur in world. Here, we propose new health monitoring techniques to the prediction of heart failures. In this, we propose edge-computing based Complex Event Processing (CEP) techniques with the Remote Patient Monitoring (RPM) for the remote healthcare applications. This approach is based on the CEP it is combined with the statistical approach. For the extraction heart defects of patients C4.5 algorithm and, to the prediction of heart failure multilayer perceptron (MLP) model will be consider. First phase is to collects health parameters. Second phase is to process the collected data using an analysis rule. This proposed system continuously monitors heart patient and it predicts heart failures strokes based on the related symptoms. When a critical condition occurs then it alters patients and cardiologist.
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