# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 5 Method
### Classifiers  
In this study, as illustrated in Figure 2, a variety of classification algorithms were employed to address the diagnostic challenges associated with AAS. These classifiers include random forest (RF), logistic regression (LR), Gradient Boosting Classifier (G-Boost), Extreme Gradient Boosting (XGB), K-Nearest Neighbors (KNNs), decision trees, Gaussian Naive Bayes (Gaussian-NB), and AdaBoost. Each algorithm offers unique capabilities in handling the complexity of clinical data and extracting valuable insights for accurate AAS diagnosis. The application of these classifiers aims to establish a comprehensive framework for categorizing patients and assisting clinicians in making informed decisions regarding AAS diagnosis and management. One widely employed technique is random forest (RF), recognized for its ensemble learning approach, which constructs multiple decision trees on random subsets of features and data points. By mitigating overfitting and enhancing generalization, random forest offers robustness and insightful feature importance analysis. Logistic regression (LR) stands out as a straightforward, yet powerful algorithm suited for binary classification tasks. It estimates probabilities based on predictor variables, providing interpretability and effectiveness. Gradient Boosting Classifier (G-Boost) sequentially combines weak learners, refining predictions through gradient descent optimization. Its iterative approach allows it to capture intricate data patterns, making it valuable for tasks like diagnosing AAS. Similarly, Extreme Gradient Boosting (XGB) merges decision trees with regularization techniques, offering high performance and interpretability, especially in handling numerical clinical features for AAS diagnosis. K-Nearest Neighbor (KNN) classifies instances based on the majority class of their nearest neighbors, proving valuable for assessing complex clinical manifestations and determining the likelihood of AAS based on similar cases. Decision trees provide hierarchical decision-making, capturing non-linear relationships within data, essential for understanding AAS diagnostic logic and interpreting predictive factors. Gaussian Naive Bayes leverages [PERSON] theorem and assumes feature independence, making it efficient for handling numerical clinical variables and aiding in probabilistic classification for AAS diagnosis. Lastly, AdaBoost sequentially trains weak learners, emphasizing challenging instances to improve accuracy iteratively, contributing to more accurate classification outcomes in AAS diagnosis. Overall, these classification methods offer diverse approaches to handling AAS diagnostic challenges, providing valuable insights and accurate predictions based on clinical data. In addition, we selected widely used machine learning algorithms, such as random forest, XGBoost, Gradient Boosting, and logistic regression, based on their strong performance in similar medical prediction tasks. To improve model performance, we used grid search with 10-fold cross-validation for hyperparameter tuning. For example, in random forest, we tested different numbers of trees (from 50 to 500) and tree depths (5 to 20). For XGBoost, we adjusted learning rates (0.01 to 0.2) and tree depths (3 to 10). These tuning steps helped us find the best model settings for our dataset. We have conducted a basic statistical analysis to compare the performance of the different classification models used in our study. We calculated the mean accuracy and 95% confidence intervals (CI) for each classifier using 10-fold cross-validation. Here are the results of our statistical comparison:  
* Random forest (RF): Mean accuracy = 99.2%, CI = [99.1%, 99.3%]. * Decision trees (DT): Mean accuracy = 98.9%, CI = [98.7%, 99.1%]. * K-Nearest Neighbors (KNN): Mean accuracy = 98.2%, CI = [98.1%, 98.4%]. * Extreme Gradient Boosting (XGB): Mean accuracy = 93.9%, CI = [88.7%, 99.0%]
* Gradient Boosting (G-Boost): Mean accuracy = 91.5%, CI = [85.7%, 97.2%]
* AdaBoost: Mean accuracy = 83.4%, CI = [78.9%, 87.8%].