# Early Risk Prediction of Diabetes Based on GA-Stacking
## 4 Results
### Experiments and Results
#### 4.3.1 Evaluation between Different Models on Qingdao Physical Examination Dataset  
In order to have a clear understanding of the correlation of the attributes in the Qingdao physical examination dataset, we have drawn a feature correlation heatmap, as shown in Figure 3. The bluer the color in the picture, the smaller the correlation, and the more purple the color, the higher the correlation. It can be seen from Figure 3 that the redundancy between most of the attributes is small, but the redundancy between a small part of the attributes is relatively large. So, it is necessary to extract a representative attribute subset. Table 1 shows the performance comparison of KNN, SVM, LR, NB, CNN, and stacking using GA or not. It can be seen from the table that adding GA before data input to the model can reduce the prediction time, while the accuracy, precision, sensitivity, specificity, and F1-score increase by more than 0.1%. It also shows that on the redundant dataset, using GA not only effectively improves the prediction efficiency, but is also helpful to improve the performance of the model. Comparing with the performance of KNN, SVM, LR, NB, and CNN, GA-CNN showed the best performance. The average accuracy is 85.08%, and the average precision, sensitivity, specificity, and F1-score are 100%, 30.10%, 100%, and 46.27%, respectively. The performance of GA-SVM is second only to GA-CNN, with an accuracy of 84.48%, and its precision, sensitivity, specificity, and F1-score are better than other machine learning  
Figure 3: Feature correlation heatmap. algorithms. Finally, SVM is selected as another primary learner of stacking, so that the proposed algorithm could extract potential information while being suitable for binary classification problems. Table 1 shows the performance of the proposed GA-stacking on the Qingdao physical examination dataset. The average accuracy is 85.88%, and the average precision, sensitivity, specificity, and F1-score are 96.12%, 39.24%, 99.92%, and 55.73%, respectively. Comparing the performance of machine learning algorithms in the table, the accuracy has increased by more than 1%, and the F1-score has increased by more than 7%. The above results indicate that GA-stacking is superior to other machine learning methods in performance metrics such as accuracy, precision, recall, and F1-score. So, GA-stacking is more suitable for the early prediction of diabetes. 3.2 Evaluation between Different Models on the Early-Stage Diabetes Risk Prediction Dataset Published by UCI  
In addition, we also verified the generalization ability of GA-stacking on the early-stage diabetes risk prediction dataset published by UCI. To obtain higher-accuracy results, data preprocessing is performed, which includes coding, discretization, normalization, and dataset division. Table 2 lists the performance comparisons of KNN, SVM, LR, NB, CNN, and GA-stacking. The model we proposed is more effective in predicting diabetes risk. In terms of accuracy, compared with traditional machine learning algorithms, it has been improved. It is also 9.61%, 5.17%, and 1.92% higher than NB, LR, and KNN, respectively. In addition, comparing the accuracy of the two primary learners of CNN and SVM, the accuracy has been increased by 1.92% and 1.91%. In terms of precision, sensitivity, and FI score, GA-stacking reached 100%, 96.77%, and 98.36%. Except for the slightly lower sensitivity, all other performance metrics achieved the best results. Several similar works are available in the literature. Table 3 presents the comparative results between studies. The system architecture proposed by [PERSON] et al. used NB, J48, LR, and RF for risk prediction. Among them, RF had the highest accuracy