# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 2 Literature Review of AAS
### Literature Review Summary
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Variable** & **Sample Size** & **Preprocessing** & **Methods** & **Results** \\ \hline [PERSON] [13] & 32 & 976 & 5-fold cross-validation & Logistic regression & AUC: 0.73 & Sensitivity: 99\% \\  & & & Super Learner (SL) & Specificity: 18\% \\ \hline [PERSON]. [PERSON] [14] & 15 & 492 & CFS feature selection & Bayesian Network, Naive & AUC: 0.857 & Sensitivity: 93.1\% \\  & & 10-fold cross-validation & Bayes, SMO & Sensitivity: 93.1\% & Specificity: 70.2\% \\ \hline [PERSON] [15] & 16 & 1133 & Divided the sample into training (70\%) and testing (30\%) data & Random forest & AUC: 0.752 & Sensitivity: 99\% \\  & & & (70\%) and testing (30\%) data & & Specificity: 51.4\% \\ \hline [PERSON]. [PERSON] [16] & 41 & 1344 & Treatment Encoding & Logistic regression & AUC: 0.927 & AUC: 0.927 \\  & & & Type of AAD and Gender Encoding & Decision tree & Sensitivity: 72.9\% \\  & & & Binary Feature Encoding & Extreme Gradient & Specificity: 96.6\% \\  & & & 10-fold cross-validation & Boosting Nearest neighbor & Accuracy: 0.918 \\ \hline [PERSON]. [PERSON] [17] & 23 & 380 & \begin{tabular}{c} Logistic regression \\ Decision tree \\ Extreme Gradient \\ Boosting \\ Random Forest (RF) \\ Support Vector \\ Machine (SVM) \\ \end{tabular} & \begin{tabular}{c} \\ \end{tabular} & \begin{tabular}{c} \\ \end{tabular} \\ \hline Y. [PERSON] [18] & 10 & 200 & \begin{tabular}{c} Shapiro-Wilk test used to \\ determine normal distribution. \\ Normal distribution data \\ presented as \\ (Mean SD), compared with \(t\)-tests. \\ Non-normal distribution data \\ presented as median (M) and \\ interquartile range, compared \\ by two \\ independent-sample \\ rank-sum tests. \\ \end{tabular} & \begin{tabular}{c} Logistic regression \\ Random Forest \\ SVM \\ CNN \\ \end{tabular} & \begin{tabular}{c} AUC: 0.99 \\ Sensitivity: 93\% \\ Specificity: 90\% \\ Accuracy: 0.90 \\ \end{tabular} \\ \hline [PERSON] [19] & 22 & 638 & \begin{tabular}{c} Patients categorized into subgroups \\ based on the onset time (\textless{}2\) h and \\ \(\geq\)2 h). \\ \end{tabular} & \begin{tabular}{c} Multivariate logistic \\ \end{tabular} & AUC: 0.974 \\ [PERSON] [19] & 22 & 638 & \begin{tabular}{c} Multivariate logistic \\ regression \\ \end{tabular} & \begin{tabular}{c} AUC: 0.974 \\ Sensitivity: 91.8\% \\ Specificity: 91.3\% \\ \end{tabular} \\ [PERSON] [19] & 22 & 638 & \begin{tabular}{c} Pretest Probability Assessment \\ Median D-dimer levels compared \\ between patients with non-AAS \\ and alternative diagnoses \\ \end{tabular} & \begin{tabular}{c} AUC: 0.74 \\ Sensitivity: 65\% \\ Specificity: 83\% \\ \end{tabular} \\ \hline [PERSON] [21] & 44 & 5548 & \begin{tabular}{c} Constructing exact binomial 80\% \\ confidence intervals around key \\ proportions. \\ Handling missing data in the \\ calculation of performance indices \\ for clinical decision tools (CDTs). \\ \end{tabular} & \
\begin{tabular}{c} Multicenter observational \\ EDs assessing for \\ potential AAS, using both \\ prospective and \\ retrospective recruitment, \\ with anonymized patient \\ data collection \\ \end{tabular} &
\begin{tabular}{c} AUC: 0.958 \\ Sensitivity: 95\% \\ Specificity: 97\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work summary. One limitation observed in the above-mentioned studies is the absence of a sample size calculation. This study recognizes the importance of having at least 10 events for each predictor parameter when developing prediction models, as small sample sizes can compromise statistical power and the reliability of predictive models. However, this