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. 2022 Jan 10:2022:1070697.
doi: 10.1155/2022/1070697. eCollection 2022.

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

K Butchi Raju  1 , Suresh Dara  2 , Ankit Vidyarthi  3 , V Mnssvkr Gupta  4 , Baseem Khan  5
Affiliations

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

K Butchi Raju et al. Comput Intell Neurosci. .

Retraction in

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.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Proposed “smart heart disease prediction system” with IoT-fog computing.
Figure 2
Figure 2
Optimized CCNN architecture for smart heart disease diagnosis.
Figure 3
Figure 3
Parameter optimization of cascaded convolutional neural network with GSO algorithm.
Figure 4
Figure 4
Flowchart of GSO algorithm.
Figure 5
Figure 5
Analysis on designed smart “heart disease prediction” model with different heuristic-based algorithms in terms of “accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, FDR, F1-score, and MCC.”
Figure 6
Figure 6
Analysis on designed smart “heart disease prediction model” with different classifiers in terms of “accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, FDR, F1-score, and MCC.”
Figure 7
Figure 7
Analysis on designed smart “heart disease prediction” model with several metaheuristic techniques on k-fold validations in terms of “accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, FDR, F1-score, and MCC.”
Figure 8
Figure 8
Analysis on designed smart heart disease prediction model with different classifiers on k-fold validations regarding “accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, FDR, F1-score, and MCC.”
Algorithm 1
Algorithm 1
GSO algorithm.
See this image and copyright information in PMC

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