## 4 Discussion  
As shown in Figure 7, the data model is better trained by the oversampled data than the oversampled data without data composition. Due to the small amount of diabetic cardiovascular disease data in the original data, the weight of the model training will be biased towards the most samples, and the diseased samples in the test data will be divided into non-diseased ones. Compared with the original dataset, the accuracy, specificity, F1-score, G-mean and AUC of the sampled dataset were significantly increased. It shows that the classification effect of the balanced data based on the IOWA operator SMOTE algorithm is better than that of the unbalanced data. It shows high sample recognition ability by using data synthesis based on weight and extension algorithms. In the aspect of analyzing text medical data especially unbalanced data are synthesized by a few class samples which shows great advantages. From the data level, the results in Table 2 show that the synthesized data based on the IOWA operator SMOTE algorithm is more consistent with the characteristics of the original data than the SMOTE algorithm and the ADASYN algorithm, which may improve the performance of data classification.  
The way that the data are quantified can also affect the performance of the model. In the experiment, we quantified the data in different forms. As shown in Table 3, data modification can improve the accuracy of the model in the verification set. The corrected data may make more inputs valid. Further analysis of Tables 3-5 shows that the performance of data synthesis based on weight and extension algorithms has been improved when trained using the same extended dataset, so the proposed algorithm has the powerful ability to improve model robustness. Even when there is less raw data, the proposed algorithm also has a better performance in the identification of text-like medical data and has a strong ability to effectively diagnose.  
Meanwhile, the accuracy and loss of data synthesis based on weight and extension algorithms are shown in Figure 8. As shown in Figure 9, the best performance achieved by the model on the training set is the accuracy of about 93%, the loss value of about 4% and the area under the curve (AUC) is 94.37%. It has proved that the proposed model has good learning ability and generalization ability and can judge the health status of unknown samples well. Although extended learning allows higher recognition accuracy with less relevant original data, its performance is still inferior to the model trained with very large data training. However, when a new convolutional neural network is trained with large amounts of data, it takes a lot of time to achieve good performance. In this case, it is difficult to verify the performance of each modified algorithm in time, which will take a lot of time. Although the amount of data in the extended dataset has increased compared with the original dataset, the total amount is still relatively small, so the training time of the model is correspondingly less. Therefore, the extended learning algorithm can also be more efficient in training time than the large-scale dataset. When the original data are small, data synthesis based on weight and extension algorithms is a better choice. Meanwhile, it is a  
Figure 9: ROC curve of data synthesis based on weight and extension algorithms.  
very difficult task to collect enough raw medical data to train a blank convolutional neural network. Therefore, data synthesis based on weight and extension algorithms is applied to the pre-training of neural networks to achieve better results in less time before using a large amount of other medical data.