# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 6 Results
### Discussion  
In comparison to the prior literature on the prediction of Acute Aortic Syndromes (AAS) using machine learning and Deep leaning techniques, our study presents notable advancements in methodology, data handling, and scale. [PERSON] et al. [13] employed  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Features Name** & **Importance Value** \\ \hline Age & 0.1516 \\ Troponin & 0.1471 \\ Temperature & 0.1285 \\ Weight—kilogram & 0.124 \\ Height—cm & 0.1115 \\ Respiratory rate & 0.1092 \\ Body mass index & 0.085 \\ Pulse Ox & 0.0722 \\ Pain score & 0.0676 \\ Gender & 0.0033 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Top 10 important features of the Relief method.  
Figure 8: Top 10 important features with Relief method.  
logistic regression and the Super Learner (SL) ensemble method, with SL achieving an AUC of 0.73 using 5-fold cross-validation on a dataset comprising 976 patients, including 609 positive cases.  
[PERSON] et al. [14] implemented Correlation-Based Feature Selection (CFS) and identified the Bayesian Network as the most effective model, achieving an AUC of 0.857 with a sample size of 492. Similarly, [PERSON] et al. [15] explored the predictive value of D-dimer and radiographic features using both multivariate logistic regression and random forest models. Their study, conducted on 558 patients with 93 positive cases, reported an AUC of 0.994 for the random forest model in predicting in-hospital rupture of type A aortic dissection.  
[PERSON] et al. [16] demonstrated the predictive capability of the XGBoost algorithm in assessing in-hospital mortality risk among AAS patients, achieving an AUC of 0.927. They further validated XGBoost's performance using 10-fold cross-validation on a dataset of 1344 patients, where it outperformed other models in terms of accuracy and robustness.  
[PERSON] et al. [18] applied a Convolutional Neural Network (CNN) to predict rupture risk in acute type A aortic dissection, achieving an AUC of 0.99. However, the model was trained and validated on a relatively small dataset of 200 patients, which may limit its generalizability.  
In contrast, our study integrates a broad range of preprocessing techniques, including multiple imputation (MICE) for handling missing data; Synthetic Minority Oversampling Technique (SMOTE) for class imbalance; and various feature selection methods, such as Relief. Multiple machine learning classifiers were evaluated, and the optimal combination-- Relief feature selection with random forest--achieved superior performance with an AUC of 1.0 and accuracy of 99.3%. This comprehensive pipeline allowed us to effectively model a real-world, high-dimensional, and imbalanced clinical dataset.  
Moreover, the scale of our dataset represents a significant contribution to the field. While the largest sample size identified in the previous literature was 5548 patients in the study by [PERSON] [21], our integrated dataset comprises 148,707 patient records collected from 68 emergency departments. Of these, 148,578 patients were non-AAS cases, while 129 were confirmed AAS cases. This extensive dataset not only enhances the statistical power of our analysis but also provides a valuable foundation for future research in AAS prediction and broader cardiovascular risk modeling.  
The scale of this research dataset represents a substantial contribution to the field of AAS prediction. As shown in Figure 9, which compares sample sizes across related studies, the largest previously reported dataset included 5548 patients in the study by [PERSON] [21]. In contrast, our integrated dataset comprises 148,707 patient records collected from 68 emergency departments, including 129 confirmed AAS cases and 148,578 non-AAS cases. This significant increase in sample size not only strengthens the statistical power of our findings but also enhances the generalizability and clinical relevance of our machine learning models. Moreover, this extensive dataset provides a valuable resource for future research on AAS detection and broader cardiovascular risk prediction.