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.

中文翻译：

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基于改进的深度卷积神经网络的流行病大数据研究。

近年来，随着衰老过程的加速和生活压力的加剧，慢性流行病的比例逐渐增加。糖尿病患者住院期间将产生大量医学数据。在医学数据中发现潜在的医学规律和有价值的信息将具有重要的现实意义和社会价值。有鉴于此，提出了一种改进的深度卷积神经网络算法（简称“ CNN +”）来预测糖尿病的变化。首先，使用袋装综合分类算法代替深层CNN的输出层功能，可以为糖尿病患者数据集构建改进的深层CNN算法，提高分类的准确性。通过这种方式，“ CNN +”算法可以同时利用深度CNN和装袋算法的优势。一方面，它可以利用深层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，套袋综合分类算法可用于特征分类，以提高分类的准确性，获得更好的疾病预测效果，以帮助医生进行诊治。实验结果表明，与传统的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。套袋综合分类算法可用于特征分类，提高分类精度，取得更好的疾病预测效果，协助医生进行诊治。实验结果表明，与传统的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。套袋综合分类算法可用于特征分类，提高分类精度，取得更好的疾病预测效果，协助医生进行诊治。实验结果表明，与传统的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。