Abstract
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 %.
Highlights
In today’s world, heart disease is the leading impact of death to all the age groups
The Enhanced Deep learning assisted Convolutional Neural Network (EDCNN) system has been implemented on the Internet of Medical Things Platform (IoMT) for a decision support system, which helps doctors to diagnose heart patient’s information in the cloud platform effectively and the Test results show that a flexible design with hyperparameters can achieve a precision of up to 99.1 %
As early and accurate prediction of heart disease is essential for early intervention and extended long-term survival, this high Likelihood sensitivity scoring along with the relatively high 0.8571 and 0.8922 AUC scoring indicates a high accuracy in the diagnosis of heart disease in patients in developing Deep Neural Network (DNN) models
Summary
In today’s world, heart disease is the leading impact of death to all the age groups. A. OUTCOMES OF THE RESEARCH To overcome these issues, in this paper, an Enhanced deep convolutional neural network (EDCNN) has been proposed for the early detection of heart disease and diagnosis. THE SCOPE OF THIS RESEARCH IS STATED AS FOLLOWS To determine the accuracy in recognition of heart disease using an enhanced deep learning assisted convolutional neural network approach has been proposed. In section 2: The Enhanced Deep learning assisted Convolutional Neural Network (EDCNN) has been proposed to assist and improve patient accuracy and reliability in diagnosis and prognostics of heart disease. The activation function determines each neuron’s weighted inputs and reduces the number of layers to two layers, by varying the weights that are assigned to a perceptron
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
