A Smart and Intelligent Way of Diagnosing of Heart Disease in Short Span of Time Through Integrated Algorithms of IoT, FC and DL

Chronic diseases including cancer, diabetes, heart disease, and respiratory disorders are major hazards to human health in the global context. Particularly heart disease may be difficult to diagnose because of its wide range of characteristics and symptoms. Concurrently, feature extraction is carrie...

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Published in2024 1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS) pp. 1 - 6
Main Authors Malviya, Ashwini, Singhal, Rishi, S, Asha
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.04.2024
Subjects
Accuracy
Cardiac Disease Prediction
Computational modeling
Deep Learning
Edge-Fog-Cloud Computing
Feature extraction
Fog Computing
GSO-CCNN Model
Healthcare Applications
Heart
Iot
Medical services
Predictive models
Real-Time Processing and Ensemble Learning
Real-time systems
Smart Healthcare
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DOI10.1109/ISTEMS60181.2024.10560242

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Summary:Chronic diseases including cancer, diabetes, heart disease, and respiratory disorders are major hazards to human health in the global context. Particularly heart disease may be difficult to diagnose because of its wide range of characteristics and symptoms. Concurrently, feature extraction is carried out for other pertinent features. Combining these characteristics, an Optimised Passed on Convoluted Neural Network (CCNN) is used as a diagnostic system. The performance of the CCNN is increased by optimising its hyperparameters using Galactic Swarm Optimisation (GSO). Performance study highlights the advantages of the suggested GSO-CCNN model, which outperforms other models in terms of accuracy. This means that the recommended approach is more efficient than traditional models, as shown by the thorough comparison study.
DOI:10.1109/ISTEMS60181.2024.10560242