Institute of Traditional Chinese Medicine, Ningxia Medical University, Yinchuan 750000, China.
Weifang Engineering Vocational University, Weifang, Shandong Province, 262500, China.
Comput Math Methods Med. 2022 Jul 27;2022:3206378. doi: 10.1155/2022/3206378. eCollection 2022.
As a typical disease, cardiovascular and cerebrovascular diseases cause great damage to the human body. In view of the problem that the existing models failed to describe and represent the characteristics of cardiovascular and cerebrovascular indicators, convolution neural network was used to analyze the metabolic factors of cardiovascular and cerebrovascular. Based on convolutional neural network theory, feature extraction was carried out on the relevant parameters of the model, and the change trend of different cardiovascular and cerebrovascular indicators was studied by model optimization, theoretical analysis, and experimental verification. Relevant studies show that the value of neurons increases slowly at first and then rapidly with the increase of bias term . And with the increase of computing time, the corresponding nonlinear characteristics are gradually reflected; so, the influence of computing time on neuron results should be considered when selecting bias term . The gradient changes under different functions have typical symmetry, which indicates that the effects of functions on model parameters have certain cyclic characteristics. Among them, ReLU function has the largest variation range, tanh function has a relatively small curve variation range, and sigmoid function has the smallest variation range. Five indicators are selected to describe the metabolic characteristics of the disease through characteristic analysis of cardiovascular and cerebrovascular diseases. The onset signs have the greatest impact on cardiovascular and cerebrovascular diseases, while the corresponding metabolic characteristics have the least impact on cardiovascular and cerebrovascular diseases. The study showed that the influence of different indicators on the model had typical stage characteristics, and relevant data were used to verify the accuracy of the model. Finally, the optimization model based on convolutional neural network was used to predict the metabolic characteristics of cardiovascular and cerebrovascular diseases. Relevant studies show that the optimization model can better analyze the metabolic characteristics of cardiovascular and cerebrovascular diseases. This research can provide theoretical support for the application of convolutional neural networks in other fields.
作为一种典型疾病，心脑血管疾病对人体造成了巨大的危害。针对现有模型未能描述和表示心脑血管指标特征的问题，利用卷积神经网络对心脑血管的代谢因素进行分析。基于卷积神经网络理论，对模型的相关参数进行特征提取，通过模型优化、理论分析和实验验证研究不同心脑血管指标的变化趋势。相关研究表明，神经元的价值起初增长缓慢，然后随着偏置项的增加而迅速增长。随着计算时间的增加，相应的非线性特征逐渐得到体现；因此，在选择偏置项时，应考虑计算时间对神经元结果的影响。不同函数下的梯度变化具有典型的对称性，这表明函数对模型参数的影响具有一定的循环特征。其中，ReLU 函数的变化范围最大，tanh 函数的曲线变化范围较小，sigmoid 函数的变化范围最小。通过心脑血管疾病的特征分析，选择五个指标来描述疾病的代谢特征。发病迹象对心脑血管疾病的影响最大，而相应的代谢特征对心脑血管疾病的影响最小。研究表明，不同指标对模型的影响具有典型的阶段性特征，并使用相关数据验证了模型的准确性。最后，利用基于卷积神经网络的优化模型对心脑血管疾病的代谢特征进行预测。相关研究表明，优化模型能够更好地分析心脑血管疾病的代谢特征。这项研究可以为卷积神经网络在其他领域的应用提供理论支持。
