Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model

Abstract

Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diabetes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. Because of the emergence of smart wearable gadgets, fog computing and “Internet of Things” (IoT) solutions have become necessary for diagnosis. The proposed model integrates Edge-Fog-Cloud computing for the accurate and fast delivery of outcomes. The hardware components collect data from different patients. The heart feature extraction from signals is done to get significant features. Furthermore, the feature extraction of other attributes is also gathered. All these features are gathered and subjected to the diagnostic system using an Optimized Cascaded Convolution Neural Network (CCNN). Here, the hyperparameters of CCNN are optimized by the Galactic Swarm Optimization (GSO). Through the performance analysis, the precision of the suggested GSO-CCNN is 3.7%, 3.7%, 3.6%, 7.6%, 67.9%, 48.4%, 33%, 10.9%, and 7.6% more advanced than PSO-CCNN, GWO-CCNN, WOA-CCNN, DHOA-CCNN, DNN, RNN, LSTM, CNN, and CCNN, respectively. Thus, the comparative analysis of the suggested system ensures its efficiency over the conventional models.

Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

“…For predicting heart disorders, the suggested model employs the CCNN [2]. It takes into account into data properties.…”

“…The GSO [2] method is used in the suggested automated healthcare model with IoT-assisted Healthfogedge computing to improve the prediction rate of cardiac disorders. It is employed in the optimization of the cascaded network's hidden layers, hidden neurons, and transfer functions.…”

“…Both industry and academia have adopted these subjects as vital components. Due to the considerable time delay, cloud computing is not ideal for implementations to get answers [2]. Modern innovations such as Big Data, fog computing, the Internet of Things, and edge computing have exploded in popularity due to their capability to provide a variety of performance parameters focused on the target workloads.…”

An Automated Heart Disease Diagnosis System Using Adaptive Cross-Layer Stacked Residual Convolutional Neural Networks

“…The findings of the suggested methodology would act as the benchmark for future investigations on heart problems. In [33], the Edge-Fog-Cloud computing architecture has been used to offer a new smart healthcare concept. Predictive systems for diagnosing cardiac disease could be improved in the future by including more effective feature selection methods, optimization approaches, and classification methods.…”

A Novel IoT-based Monitoring Device for Effective Prediction of Heart Failure

“…Public awareness campaigns centered on these themes have the possibility of significantly improving outcomes for patients with heart failure and, eventually, saving lives. Improved outcomes for heart failure patients are also related to adherence to clinical practice guidelines [3]. Nevertheless, the degree to which physicians adhere to suggestions from guidelines varies significantly across several nations.…”