An IoT-Fog-Cloud Integrated Framework for Real-Time Remote Cardiovascular Disease Diagnosis

Abstract

Recently, it has proven difficult to make an immediate remote diagnosis of any coronary illness, including heart disease, diabetes, etc. The drawbacks of cloud computing infrastructures, such as excessive latency, bandwidth, energy consumption, security, and privacy concerns, have lately been addressed by Fog computing with IoT applications. In this study, an IoT-Fog-Cloud integrated system, called a Fog-empowered framework for real-time analysis in heart patients using ENsemble Deep learning (FRIEND), has been introduced that can instantaneously facilitate remote diagnosis of heart patients. The proposed system was trained on the combined dataset of Long-Beach, Cleveland, Switzerland, and Hungarian heart disease datasets. We first tested the model with eight basic ML approaches, including the decision tree, logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, AdaBoost, and XGBoost approaches, and then applied ensemble methods including bagging classifiers, weighted averaging, and soft and hard voting to achieve enhanced outcomes and a deep neural network, a deep learning approach, with the ensemble methods. These models were validated using 16 performance and 9 network parameters to justify this work. The accuracy, PPV, TPR, TNR, and F1 scores of the experiments reached 94.27%, 97.59%, 96.09%, 75.44%, and 96.83%, respectively, which were comparatively higher when the deep neural network was assembled with bagging and hard-voting classifiers. The user-friendliness and the inclusion of Fog computing principles, instantaneous remote cardiac patient diagnosis, low latency, and low energy consumption, etc., are advantages confirmed according to the achieved experimental results.

En-MinWhale: An Ensemble Approach Based on MRMR and Whale Optimization for Cancer Diagnosis

“…One of the widely used fundamental ensemble approaches to boost the effectiveness of the proposed investigation is voting. Hard and soft voting are the two main styles of voting [43,44]. The plurality method is the most basic kind of voting, allowing each voter to select only one option-their most favored choice.…”

Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

“…In addition, the aged, the chronically ill, and the physically disabled increasingly demand a healthcare system that can provide a reliable, all-encompassing continuous health monitoring system [ 9 , 10 ]. Several academic research articles claim that remote health monitoring systems finally allow doctors to keep tabs on their patients in a timely and accurate manner [ 11 , 12 , 13 ]. In Figure 1 , the diffusion of Cloud-based Fog to IoT end devices is shown.…”

IFCnCov: An IoT‐based smart diagnostic architecture for COVID‐19

“…New study avenues have opened up as a result of automated disease identification and prediction based on the IoT-fog-cloud (IFC) integrated concept. 5,6 • Building a portable COVID-19 patient diagnosis system using the ensemble deep learning (EDL) technique;…”

“…The cloud layer's processing and storing operations can be partly performed by network devices like servers and routers in the fog layer. New study avenues have opened up as a result of automated disease identification and prediction based on the IoT‐fog‐cloud (IFC) integrated concept 5,6 …”