论文标题
基于MDCNN分类器的心脏病预测的IoT框架
An IoT Framework for Heart Disease Prediction based on MDCNN Classifier
论文作者
论文摘要
如今，心脏病是全球死亡的主要原因。预测心脏病是一项复杂的任务，因为它需要经验以及高级知识。最近在医疗保健系统中采用了物联网（IoT）技术来收集传感器值以进行心脏病诊断和预测。许多研究人员专注于心脏病的诊断，但诊断结果的准确性很低。为了解决这个问题，提出了一个IoT框架，以使用改良的深卷积神经网络（MDCNN）更准确地评估心脏病。附着在患者身上的智能手表和心脏监护设备可监视血压和心电图（ECG）。 MDCNN用于将接收到的传感器数据分类为正常和异常。通过将提出的MDCNN与现有深度学习神经网络和逻辑回归进行比较，可以分析系统的性能。结果表明，拟议的基于MDCNN的心脏病预测系统的性能比其他方法更好。提出的方法表明，对于最大记录，MDCNN的精度为98.2，比现有分类器要好。
Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.
