A Smart Healthcare System Using Internet of Things and Deep Learning for CVD Disease Diagnosis

Abstract

Today one of the major causes of death is Cardiovascular Disease (CVD) and it is required to accurately diagnosis. With the recent advents in Deep Learning (DL) and Internet of Things (IoT), the conventional healthcare systems are transformed to smart healthcare.  The IoT with DL predictive methods offers auto diagnosis and detection errors can be reduced compared to the human expertise. In this view, a smart healthcare system using IoT and Deep learning for CVD disease diagnosis is implemented in this paper. Here, a disease diagnosis technique is developed using IoT and DL convergence for smart healthcare system. The IoT devices include sensors and wearable’s permitting seamless collection of data whereas DL model uses the data in disease diagnosis. This technique utilizes Crow Search Optimization algorithm-based Deep Neural Network (CSO-DNN) learning model for disease diagnosis. This DNN learning model is based on the architecture of deeper multilayer perceptron with dropout and regularization using DL.  The CSO algorithm will be applied to tune the DNN model ‘bias’ and ‘weights’ parameters for achieving better medical data classification. Isolation Forest (iForest) method is utilized for removing the outliers. The CSO application helps to considerable improvement in CSO-DNN approach diagnostic outcomes. In result analysis the CSO-DNN model performance will be validated and exhibit high detection accuracy for DNN classification in diagnosing CVD.