# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 2 Literature Review of AAS
### Prediction of AAS by Machine Learning Techniques  
In prior investigations focusing on AAS, researchers adopted varied methodologies to collect data. Some studies relied on immediate medical experiment results and demographic data from patients arriving at emergency departments, while others concentrated on patients admitted to hospitals, incorporating more detailed parameters and epidemiological experiment results. Certain studies utilized standard data entry forms, capturing extensive information on patient demographics, history, clinical presentations, physical findings, imaging study results, and patient outcomes, including mortality. In October 2020, [PERSON] et al. [13] applied machine learning methods to prehospital research, involving 976 patients. Their study focused on prehospital triage and utilized 27 variables and patient characteristics. Two prediction models for AAS were formulated, employing logistic regression and an ensemble machine learning approach named Super Learner (SL). Under-triage, representing the percentage of AAS patients not transported to the specialized aortic center, and over-triage, indicating the percentage of patients with alternative diagnoses transported to the specialized aortic center, were key parameters studied. For internal validation, [PERSON] et al. employed five-fold cross-validation in their respective studies. The SL algorithm demonstrated superior performance in predicting AAS, attributed to its ability to comprehend intricate relationships and patterns within the data. The enhanced predictive accuracy of the SL algorithm was evident in its higher AUC value of 0.73 compared to the logistic regression model's 0.67 in the validation cohort, indicating superior predictive capabilities. In [PERSON] et al. 's [14] study, initially considering 526 patients, 34 cases were excluded from the analysis, resulting in a final population size of 492. The study utilized Correlation-Based Feature Selection (CFS) to choose a subset of attributes most relevant for classification, considering each feature's utility in predicting the class and addressing intercorrelations among them. [PERSON] et al. applied 10-fold cross-validation during hyperparameter tuning for advanced machine learning models in the training data. In their study, the Bayesian Network outperformed five other methods, achieving a superior AUC value of 0.857. This underscores the effectiveness of the Bayesian Network in their research. [PERSON] et al. [15] achieved the highest AUC for predicting in-hospital rupture of type A aortic dissection using the random forest technique. The research retrospectively evaluated 1133 consecutive patients diagnosed with Thoracic Aortic Aneurysm and Dissection (TAAD), employing the random forest method. A notable finding from the research was the exceptional performance of the random forest classification model, achieving an outstanding AUC of 0.994 for the training dataset and AUC of 0.752 for testing. This remarkable result indicates an exceptional discrimination ability in the training dataset, accompanied by sensitivity of 51.4% and a specificity of 94.5%. These metrics collectively emphasize the robust predictive capabilities of the model, highlighting its effectiveness in accurately distinguishing and classifying cases within the context of TAAD. [PERSON] et al. [16] conducted a thorough research study involving 1344 patients diagnosed with acute aortic dissection, comprising 1071 survivors and 273 non-survivors. The study utilized five machine learning models, including logistic regression, decision tree, K-Nearest Neighbor, Gaussian Naive Bayes, and Extreme Gradient Boosting (XGBoost), to predict in-hospital mortality risk. Among these models, the XGBoost model demonstrated remarkable performance, with the highest mean AUC of 0.927 across 10 iterations. The study revealed that the XGBoost model was an effective approach for generating accurate and early predictions of in-hospital mortality in patients with AAD.