Heart Disease Detection using the Internet of Medical Things

The deployment of Internet of Medical Things (IoMTs) for remote monitoring has grown exponentially as alternative for in-hospital diagnosis. The COVID-19 lockdown and the shortage of human and medical apparatus pushed the detection and diagnostics toward the Wireless Body Area Network to prevent the spread of infection. In this paper, we present a novel approach to detect cardiovascular diseases using the IoMTs. The proposed approach is based on the use of autoencoders to derive a compressed representation of input record, followed by Random Forest to classify the latent data. The objective is to provide a lightweight and accurate model compatible with the constrained resources of sensors. Our experiment results on public annotated dataset show that our approach is able to enhance the performance where the obtained area under curve is 89%. We also compare the performance of widely used classification algorithms to prove the efficiency of our proposed model.