The enormous developments in the _eld 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.
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. Test results show that a _exible design and subsequent tuning of EDCNN hyperparameters can achieve a precision of up to 99.1 %.
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