# 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
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Time** & **Acc (\%)** & **Pre (\%)** & **Sen (\%)** & **Spe (\%)** & **FI-Score (\%)** \\ \hline
**KNN** & 0.637 & 79.15 & 50.74 & 27.41 & 92.91 & 35.60 \\
**GA-KNN** & 0.614 & 81.07 & 58.22 & 35.21 & 93.27 & 43.89 \\ \hline
**SVM** & 1.671 & 84.36 & 100 & 30.37 & 100 & 46.59 \\
**GA-SVM** & 1.572 & 84.48 & 100 & 30.91 & 100 & 47.22 \\ \hline
**LR** & 0.085 & 84.07 & 95.76 & 29.57 & 99.64 & 45.18 \\
**GA-LR** & 0.067 & 84.10 & 98.21 & 30.37 & 99.85 & 46.39 \\ \hline
**NB** & 0.175 & 84.25 & 89.92 & 32.53 & 98.99 & 48.92 \\
**GA-NB** & 0.137 & 84.45 & 97.58 & 33.60 & 99.78 & 48.92 \\ \hline
**CNN** & 24.360 & 84.97 & 100 & 29.30 & 100 & 45.32 \\
**GA-CNN** & 21.857 & 85.08 & 100 & 30.10 & 100 & 46.27 \\ \hline
**Stacking** & 52.500 & 85.14 & 94.30 & 36.29 & 98.74 & 52.41 \\
**GA-stacking** & 48.652 & 85.88 & 96.12 & 39.24 & 99.92 & 55.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of different modules on the Qingdao physical examination data set. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Acc (\%)** & **Pre (\%)** & **Sen (\%)** & **Spe (\%)** & **F1-Score (\%)** \\ \hline
**KNN** & 96.79 & 93.84 & 98.38 & 95.74 & 96.06 \\
**SVM** & 96.79 & 96.72 & 95.16 & 97.87 & 95.93 \\
**LR** & 93.54 & 93.33 & 90.32 & 95.74 & 91.80 \\
**NB** & 89.10 & 90.90 & 80.64 & 94.68 & 85.47 \\
**CNN** & 96.79 & 98.86 & 93.30 & 98.93 & 95.86 \\
**GA-stacking** & 98.71 & 100 & 96.77 & 100 & 98.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of different modules on the UCI data set. rate of 97.40% [3]. [PERSON] et al. conducted experimental comparisons on seven algorithms including BN, NB, J48, RT, RF, KNN, and SVM. Among them, the accuracy of the KNN after using the 10-fold cross-validation technique to split the training data set and the test data set was the highest, reaching 98.07% [4]. [PERSON] applied neural network to diabetes risk prediction on the early-stage diabetes risk prediction dataset published by UCI. The AUC and accuracy were 98.3% and 98.1%, respectively, which were better than machine learning algorithms such as NB, KNN, SVM, and RF [5]. [PERSON] et al. proposed XBNet, using gradient boosted tree updates the weights of each layer in the neural network, which increases the model's interpretability and performance. We conducted experiments on the UCI dataset according to the code provided by the author, and the final accuracy was 80.00% [17]. Comparing [3; 4; 5], we can find that the accuracy of NB, SVM, KNN, etc., with different data preprocessing methods is lower than GA-stacking. Moreover, in [17], when XBNet is used on different datasets, the accuracy fluctuates greatly, and the generalization ability is weak. In summary, comparing the algorithms implemented in [3; 4; 5; 17] as well as KNN, SVM, LR, NB, and CNN, GA-stacking has achieved the highest accuracy rate. It is a good predictor of diabetes risk and is suitable for early diabetes risk prediction of patients.