# A Cardiovascular Disease Risk Score Model Based on High Contribution Characteristics
## 3 Results
### Scorecard Model  
The XGBH model was used to build a scorecard model for a data set containing three features. According to the principle of scorecard, the value of \(score_{0}\) and PDO are taken as 600 and 20 respectively, then the highest score for medical scoring of the test set through the scoring card constructed by the model is 754, and the lowest score is 586. The dataset is equally divided into 20 groups according to interval prevalence and score, draw the number of sick and non sick patients in each group as shown in Figure 2. The group with high interval prevalence rate corresponds to a smaller group number, the prevalence curve is gradually reduced, and the overall number of patients is increasing.  
Figure 1: ROC curve.One feature(ap_hi), Three features(ap_hi, chol,age), Five features(ap_hi, chol,age, ap_lo,BMI.  
\begin{table}
\begin{tabular}{c l l l} \hline
**Model** & **Logistic Regression** & **Random Forest** & **XGBoost** & **XGBH** \\ \hline
1 & Ap\_hi (0.1383 \(\pm\) 0.0070) & Ap\_hi (0.1326 \(\pm\) 0.0028) & Ap\_hi (0.13750 \(\pm\) 0.0065) & Ap\_hi (0.1406 \(\pm\) 0.0047) \\
2 & Weight (0.1218 \(\pm\) 0.0056) & Chol (0.0302 \(\pm\) 0.0052) & Chol (0.0321 \(\pm\) 0.0050) & Chol (0.0358 \(\pm\) 0.0034) \\
3 & BMI (0.0473 \(\pm\) 0.0030) & Age (0.0239 \(\pm\) 0.0058) & Age (0.0268 \(\pm\) 0.0030) & Age (0.0276 \(\pm\) 0.0043) \\
4 & Height (0.0434 \(\pm\) 0.0050) & Active (0.0024 \(\pm\) 0.0016) & Ap\_lo (0.0059 \(\pm\) 0.0007) & Ap\_lo (0.0063 \(\pm\) 0.0007) \\
5 & Age (0.0319 \(\pm\) 0.0079) & Ap\_lo (0.0023 \(\pm\) 0.0026) & BMI (0.0045 \(\pm\) 0.0026) & BMI (0.0036 \(\pm\) 0.0023) \\
6 & Chol (0.0012 \(\pm\) 0.0008) & Smoke (0.0008 \(\pm\) 0.0017) & Active (0.0020 \(\pm\) 0.0011) & Active (0.0034 \(\pm\) 0.0017) \\
7 & Smoke (0 \(\pm\) 0.0000) & Gender (0.0002 \(\pm\) 0.0043) & Height (0.0018 \(\pm\) 0.0015) & Gender (0.0016 \(\pm\) 0.0010) \\
8 & Alco (\(-\)0.0000 \(\pm\) 0.0001) & Alco (\(-\)0.0000 \(\pm\) 0.0008) & Gender (0.0014 \(\pm\) 0.0009) & Gluc (0.0015 \(\pm\) 0.0011) \\
9 & Active (\(-\)0.0001 \(\pm\) 0.0004) & Gluc (\(-\)0.0012 \(\pm\) 0.0014) & Smoke (0.0012 \(\pm\) 0.0009) & Smoke (0.0012 \(\pm\) 0.0005) \\
10 & Gluc (\(-\)0.0001 \(\pm\) 0.0003) & BMI (\(-\)0.0092 \(\pm\) 0.0022) & Weight (0.0010 \(\pm\) 0.0024) & Weight (0.0010 \(\pm\) 0.0007) \\
11 & Gender (\(-\)0.0002 \(\pm\) 0.0003) & Height (\(-\)0.0100 \(\pm\) 0.0049) & Alco (0.0003 \(\pm\) 0.0003) & Height (0.0010 \(\pm\) 0.0012) \\
12 & Ap\_lo (\(-\)0.0003 \(\pm\) 0.0006) & Weight (\(-\)0.0125 \(\pm\) 0.0023) & Gluc (0.0002 \(\pm\) 0.0005) & Aclo (0.0004 \(\pm\) 0.0006) \\ \hline \end{tabular}
\end{table}
Table 3: Feature importance of each prediction.  
KS evaluates the risk discrimination ability of the model by measuring the difference between the cumulative distributions of good and bad samples. Greater than 0.3 indicates that the model has strong discriminative ability. The performance graph of the scorecard model is plotted according to the value of KS as shown in Figure 3. It can be seen from the table that the maximum KS value in group 9 indicates that this group has the best effect on distinguishing normal samples from diseased samples. The passing rate of about 95% is used as the criterion for whether or not to be sick, and the fractions correspond to [683, 689]. If the sample is lower than 683, it is judged to be sick, and further medical tests such as electrocardiogram and cardiac color ultrasound are required to achieve effective prevention and treatment for cardiovascular disease.  
Figure 3: Performance graph of the scorecard model. The blue line represents the passing rate within the group, the orange line represents the change in the KS value, and the purple line represents the probability of disease within the group.  
Figure 2: Scoring results and the number of patients.