Abstract
Due to the increasing of population, the number of hospital visits by patients are increasing which puts a pressure on hospitals. Nowadays, the need for taking care of patients while they are at home is essential. Internet of Things (IoT) has been widely used in different areas such as healthcare and smart homes. IoT will assist in minimizing the hospital burden of frequent patients' visits. Applying IoT in healthcare will improve the efficiency and effectiveness, bring economic benefits, and reduce human exertions. It is well known that the best health monitoring system is able to detect abnormalities and able to make diagnosis without human exertion. However, this kind of system is dealing with health conditions which are essential and sensitive that require high accuracy to be reliable. This paper presents an Electrocardiogram (ECG) monitoring framework that overcome the accuracy limitation. Signal processing and feature extraction are applied. For the diagnosis purpose a classification stage is made in two ways; threshold values and machine learning to increase the accuracy. Experiment results reveal that the proposed model is more accurate in the diagnosis of heart diseases than other researches which makes it more confident to rely on from health experts point of view.