Bibliographic Details
| Title: |
Enhanced Deep Learning Assisted Convolutional Neural Network for Heart Disease Prediction on the Internet of Medical Things Platform |
| Authors: |
Yuanyuan Pan, Minghuan Fu, Biao Cheng, Xuefei Tao, Jing Guo |
| Source: |
IEEE Access, Vol 8, Pp 189503-189512 (2020) |
| Publisher Information: |
IEEE, 2020. |
| Publication Year: |
2020 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Heart disease prediction, convolutional neural network, deep learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
The diagnosis of heart disease has become a difficult medical task in the present medical research. This diagnosis depends on the detailed and precise analysis of the patient's clinical test data on an individual's health history. The enormous developments in the field of deep learning seek to create intelligent automated systems that help doctors both to predict and to determine the disease with the internet of things (IoT) assistance. Therefore, the Enhanced Deep learning assisted Convolutional Neural Network (EDCNN) has been proposed to assist and improve patient prognostics of heart disease. The EDCNN model is focused on a deeper architecture which covers multi-layer perceptron's model with regularization learning approaches. Furthermore, the system performance is validated with full features and minimized features. Hence, the reduction in the features affects the efficiency of classifiers in terms of processing time, and accuracy has been mathematically analyzed with test results. The EDCNN system has been implemented on the Internet of Medical Things Platform (IoMT) for decision support systems which helps doctors to effectively diagnose heart patient's information in cloud platforms anywhere in the world. The test results show compared to conventional approaches such as Artificial Neural Network (ANN), Deep Neural Network (DNN), Ensemble Deep Learning-based smart healthcare system (EDL-SHS), Recurrent neural network (RNN), Neural network ensemble method (NNE), based on the analysis the designed diagnostic system can efficiently determine the risk level of heart disease effectively. Test results show that a flexible design and subsequent tuning of EDCNN hyperparameters can achieve a precision of up to 99.1 %. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/9204704/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2020.3026214 |
| Access URL: |
https://doaj.org/article/cd199a73d5f648818604cb4dd734fb48 |
| Accession Number: |
edsdoj.199a73d5f648818604cb4dd734fb48 |
| Database: |
Directory of Open Access Journals |
