Academic Journal
Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model
| العنوان: | Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model |
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| المؤلفون: | K. Butchi Raju, Suresh Dara, Ankit Vidyarthi, V. MNSSVKR Gupta, Baseem Khan |
| المصدر: | Computational Intelligence and Neuroscience, Vol 2022 (2022) |
| بيانات النشر: | Hindawi Limited |
| سنة النشر: | 2022 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571 |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1687-5273 |
| Relation: | http://dx.doi.org/10.1155/2022/1070697; https://doaj.org/toc/1687-5273; https://doaj.org/article/436488f337eb45dca8b7da500a351287 |
| DOI: | 10.1155/2022/1070697 |
| الاتاحة: | https://doi.org/10.1155/2022/1070697 https://doaj.org/article/436488f337eb45dca8b7da500a351287 |
| رقم الانضمام: | edsbas.CE8A9C99 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 16875273 |
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| DOI: | 10.1155/2022/1070697 |