Yan'an University, College of Mathematics and Computer Science, Yan'an Shaanxi 716000, China.
Comput Math Methods Med. 2020 Jul 22;2020:3641745. doi: 10.1155/2020/3641745. eCollection 2020.
In recent years, with the acceleration of the aging process and the aggravation of life pressure, the proportion of chronic epidemics has gradually increased. A large amount of medical data will be generated during the hospitalization of diabetics. It will have important practical significance and social value to discover potential medical laws and valuable information among medical data. In view of this, an improved deep convolutional neural network ("CNN+" for short) algorithm was proposed to predict the changes of diabetes. Firstly, the bagging integrated classification algorithm was used instead of the output layer function of the deep CNN, which can help the improved deep CNN algorithm constructed for the data set of diabetic patients and improve the accuracy of classification. In this way, the "CNN+" algorithm can take the advantages of both the deep CNN and the bagging algorithm. On the one hand, it can extract the potential features of the data set by using the powerful feature extraction ability of deep CNN. On the other hand, the bagging integrated classification algorithm can be used for feature classification, so as to improve the classification accuracy and obtain better disease prediction effect to assist doctors in diagnosis and treatment. Experimental results show that compared with the traditional convolutional neural network and other classification algorithm, the "CNN+" model can get more reliable prediction results.
近年来，随着人口老龄化进程的加速和生活压力的加剧，慢性病的比例逐渐增加。糖尿病患者住院期间会产生大量的医疗数据，从中发现潜在的医学规律和有价值的信息，具有重要的现实意义和社会价值。针对这一问题，提出了一种改进的深度卷积神经网络（简称“CNN+”）算法，用于预测糖尿病的变化。首先，使用装袋集成分类算法代替深度 CNN 的输出层函数，这有助于为糖尿病患者数据集构建改进的深度 CNN 算法，并提高分类的准确性。这样，“CNN+”算法可以结合深度 CNN 和装袋算法的优势。一方面，它可以利用深度 CNN 强大的特征提取能力来提取数据集的潜在特征。另一方面，可以使用装袋集成分类算法进行特征分类，从而提高分类准确性，获得更好的疾病预测效果，辅助医生进行诊断和治疗。实验结果表明，与传统卷积神经网络和其他分类算法相比，“CNN+”模型可以获得更可靠的预测结果。
