# Attention-Oriented CNN Method for Type 2 Diabetes Prediction
## 4 Results
### Data-Preprocessing Results
#### 4.1.2 Feature Importance Analysis Results  
The feature importance analysis of the NHANES dataset and PID dataset is shown in Tables 5 and 6, respectively. First, we select and sort the features of the NHANES dataset and the PID dataset according to the F-score obtained by ANOVA. This score represents the importance of the individual characteristics of the independent variable. Among them, a total of 24 features are screened out in the NHANES dataset, and 8 features are screened out in the PID dataset. Logistic regression is then used for further risk factor assessment. In order to determine the risk and impact of individual independent variables on the dependent variable, we generate \(p\)-values for each variable to investigate the relative  
Figure 7: Correlation matrix of PID dataset and NHANES dataset. Among them (**a**) is the correlation matrix before data preprocessing of the PID dataset, (**b**) is the correlation matrix after data preprocessing of the PID dataset, (**c**) is the correlation matrix before data preprocessing of the NHANES dataset, and (**d**) is the correlation matrix after data preprocessing of the NHANES dataset.  
risk of the independent variables on the outcome variable. The \(p\)-value ranges between 0 and 1 [42]. We define a \(p\)-value as statistically significant when it is less than 0.05. Age, Blood-Rel-Diabetes, Body-Mass-Index, Systolic, HDL, Cholesterol, Hemoglobin, Diastolic, ALT, AST, Gender, Protein, and Phosphorus are important risk factors related to diabetes. Similarly, glucose, age, BMI, and blood pressure are important risk factors related to diabetes in this dataset. Through further feature screening, analysis of significant features has made a significant contribution to medical research and clinical practice, deepening the understanding of disease mechanisms, guiding early diagnosis and personalized treatment, and effectively reducing medical intervention. This analysis also helps develop prevention strategies and manage health, improving public health.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Feature** & **F-Score** & \(p\) **Value** \\ \hline
01 & Age & 2686.4097 & 0.0000 \\
02 & Blood-Rel-Diabetes & 1536.7851 & 0.0000 \\
03 & Body-Mass-Index & 1000.6701 & 0.0050 \\
04 & Systolic & 766.3686 & 0.0299 \\
05 & Weight & 526.9009 & 0.6242 \\
06 & Albumin & 525.8110 & 0.1353 \\
07 & Glycohemoglobin & 477.4174 & 0.1973 \\
08 & HDL & 378.7503 & 0.0022 \\
09 & Cholesterol & 334.9230 & 0.0000 \\
10 & GGT & 305.4725 & 0.1156 \\
11 & Hemoglobin & 270.4280 & 0.0004 \\
12 & Iron & 211.2168 & 0.9396 \\
13 & Moderate-work & 188.2664 & 0.1012 \\
14 & ALP & 156.1723 & 0.3842 \\
15 & Uric.Acid & 144.7228 & 0.3049 \\
16 & Creatinine & 102.8804 & 0.3964 \\
17 & Bilirubin & 80.4633 & 0.1825 \\
18 & Diastolic & 63.3160 & 0.0000 \\
19 & LDH & 49.8263 & 0.1427 \\
20 & ALT & 27.6458 & 0.0309 \\
21 & AST & 22.6823 & 0.0013 \\
22 & Gender & 12.2195 & 0.0048 \\
23 & Protein & 8.5813 & 0.0306 \\
24 & Phosphorus & 0.0004 & 0.0092 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Feature importance analysis results of the NHANES dataset.  
Figure 8: The relationship diagram between accuracy and category weight, where (**a**) is the NHANES dataset and (**b**) is the PID dataset.