# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 7 Conclusions  
One of the main aims of this research was to gather and clean a large clinical dataset focused on cardiovascular health, with the intention of making it useful for studies like ours. During the data preparation phase, we encountered missing values, which we handled using the Multiple Imputation by Chained Equations (MICE) method. Due to class distribution imbalance in the dataset, the SMOTE technique was used to address underrepresented classes.  
Different forms of selection of three features were employed using Principal Component Analysis (PCA), Correlational-Based feature selection (CFS), and Relief feature, with each one providing its own advantages. Therefore, an evaluation was conducted on each selected method based on the outcome, using different approaches to determine the optimal one.  
The selected features were tested against eight classification algorithms, which include Gradient Boosting, Extreme Gradient Boosting (XGB), random forest, logistic regression, K-Nearest Neighbors, decision tree, Gaussian Naive Bayes, and AdaBoost. The models were evaluated based on accuracy, AUC, precision, F1-score, sensitivity, and specificity, which reflect the model performance.  
One particularly relevant study is that by [PERSON] et al. [13], who focused on prehospital triage of AAS using machine learning. Their approach employed 5-fold cross-validation and a Super Learner model, achieving a ROC of 0.87 and sensitivity of 0.99 using a dataset of 976 hospital admissions, of which 609 were confirmed AAS cases.  
In this study, we used a data-splitting strategy that allocated 80% for training, 10% for testing, and 10% for validation. By combining this with the Relief F feature selection method and a random forest classifier, we achieved impressive results: 99.3% accuracy, 0.995 sensitivity, and 0.993 specificity, among others. This setup outperformed all the other models we tested and showed a remarkable predictive ability compared to previous research. The differences in performance across various feature selection methods really underscore how crucial it is to choose the right mix of feature selection and classification techniques that fit the dataset and the predictive task at hand. For context, one of the other standout results in the existing literature comes from [PERSON] et al. [18], who reported an accuracy of 90%, with sensitivity and specificity values of 0.93 and 0.90, respectively, using a Convolutional Neural Network (CNN). Their research pinpointed age, sex, certain biomarkers, and the aorta's morphological parameters as key independent predictors for rupture in Acute Type A Aortic Dissection (ATAAD). In our analysis, we evaluated 25 features and identified the most significant predictors through a thorough feature  
Figure 9: Sample size comparison.  
ranking process. The top-ranked variables included age, troponin levels, temperature, weight, height, and respiratory rate. Interestingly, gender also played a notable role, coming in tenth in terms of importance. These results highlight the clinical significance of regularly collected vital signs and biochemical markers in aiding the early detection of AAS in emergency-department scenarios.  
In short, regarding sample size, this study compiled an impressive clinical dataset featuring 148,707 patient records related to various cardiovascular diseases. It showcased the potential of machine learning as a game-changer for early AAS prediction in the emergency department. However, there is still a need for more research to refine these techniques and validate the findings using different classifiers or methods. By offering such a large dataset, this study sets the stage for future exploration in this area, aiming to develop effective tools for the early detection and management of AAS.