| Abstrakt: |
In today's world, Heart disease can affect more people which leads death all-over the world. Prediction of this disease without any symptoms is a complex task and it requires advance knowledge of the patient. The smart technique called Internet of Things (IoT) which collects the required details of the patient and stored in cloud server. Many research works had been carried out for heart based disease but prediction accuracy is very lower. To address this problem, a novel smart IoT-based architecture has been proposed to predict heart disease with superior accuracy using Adaptive Deep Convolution Neural Network (ADCNN) model. Primarily, the sensor devices sense the ECG and blood pressure of the patient to classify heart condition. Afterward, the ADCNN is implemented in the proposed model to classify the normal and abnormal patients. The performance of the proposed system can be compared with the existing state-of-the-art algorithms. The proposed algorithm using ADCNN classifier exposes better result with an accuracy of 99.45% than the existing classifiers. [ABSTRACT FROM AUTHOR] |
