# A Cardiovascular Disease Risk Score Model Based on High Contribution Characteristics
## 3 Results
### Feature Importance Analysis  
To estimate the contribution of each feature to the prediction, we analysed the feature importance of each prediction model using the PermutationImportance method [27]. The specific method is to randomly arrange the values of a feature column in the data set, trained the model using the disordered feature values. Feature importance is identified by looking at the extent to which the feature values affect the performance of the model. The PermutationImportance method is used to calculate the feature importance of the target variable, and then the feature importance weights are ranked and a table of feature weights is drawn as shown in Table 3. As can be seen from Table 3, the ranking of the importance of the features of the models with higher prediction accuracy is much closer. Among them, the important features of the two models with the best prediction accuracy: XGBoost and XGBH are consistent, namely systolic blood pressure (Ap_hi), cholesterol (Chol), Age, diastolic blood pressure (Ap_lo), and body mass index (BMI). And systolic blood pressure (Ap_hi) showed the greatest feature weighting of all four models, which indicated that it was the most predictive feature.  
In order to reduce the burden of patients, the previous feature importance analysis drives us to establish a simpler prediction model based on a small number of high importance features instead of the model based on all features. According to the order of feature weights of the XGBH model in Table 3, taking CVD as the target feature. The XGBH model proposed in this paper was used to make predictions based on different numbers of features and to plot ROC curves, as shown in Figure 1. As can be seen from the figure, the AUC of the model is 0.6353 [0.6262, 0.6444] when predictions are made using the most influential feature Ap_hi. Then using the top three features in terms of influence (ap_hi, chol, age), the AUC of the model reached 0.7999 [0.7926, 0.8072], and finally using the top five features in terms of influence (ap_hi, chol, age, ap_lo, BMI) for prediction, the AUC of the model reached 0.8030 [0.7957, 0.8102], with the AUC increasing from 0.6353 to 0.803 as the number of features increased.When increasing from three to five features, the AUC rise only increased from 0.7999 to 0.803, indicating that ap_lo, BMI provided a smaller  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Dataset** & **Model** & **AUC** & **Recall** & **Precision** & **F1 Score** \\ \hline \multirow{4}{*}{Without BMI} & LinearSVC & 0.651 (0.643–0.659) & 0.601 & 0.664 & 0.631 \\  & Logistic Regression & 0.697 (0.689–0.704) & 0.657 & 0.709 & 0.682 \\  & Random Forest & 0.713 (0.706–0.720) & 0.697 & 0.717 & 0.707 \\  & XGBoost & 0.802 (0.795–0.809) & 0.686 & 0.755 & 0.719 \\  & XGBH & **0.806 (0.799–0.813)** & **0.703** & **0.758** & **0.729** \\ \hline \multirow{4}{*}{With BMI} & LinearSVC & 0.656 (0.648–0.664) & 0.613 & 0.666 & 0.638 \\  & Logistic Regression & 0.712 (0.705–0.720) & 0.675 & 0.726 & 0.699 \\ \cline{1-1}  & Random Forest & 0.715 (0.707–0.722) & 0.703 & 0.717 & 0.710 \\ \cline{1-1}  & XGBoost & 0.803 (0.796–0.810) & 0.687 & 0.753 & 0.718 \\ \cline{1-1}  & XGBH & **0.807 (0.800–0.814)** & **0.704** & **0.757** & **0.730** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the dataset.  
contribution to the increase in model accuracy. Taken together, we can perform an accurate CVD risk assessment using a questionnaire with only three questions (1. blood pressure 2. Is cholesterol normal? 3. What age?).