# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 4 Clinical Dataset
### Data Loading and Exploration  
The process of loading and exploring clinical datasets in Python involves utilizing key libraries such as Pandas, NumPy, Scikit-learn, and Stats models. These libraries enable efficient data manipulation, numerical operations, visualization, machine learning tasks, and statistical modeling. By leveraging demographic information, researchers can gain insights into the distribution of the population across different age groups and genders, forming the basis for exploring patterns and trends within the dataset. This analysis facilitates a comprehensive understanding of the dataset's diversity and composition, ultimately contributing to valuable insights for medical research and healthcare decision-making. After cleaning and preprocessing the data, and based on medical knowledge and a review of the relevant literature, 26 of the most relevant variables were selected from the initial 139, as shown in Table 8. These selected variables are illustrated in Table 3 below and serve as inputs for the classifiers and machine learning methods used in this study. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Variables** & **Unit** \\ \hline
1 & AAS & \\ \hline
2 & AGE & year \\ \hline
3 & GENDER & M and F \\ \hline
4 & D-DIMER & ng/mL \\ \hline
5 & HBA1C & Percentage \\ \hline
6 & HEMOGLOBIN & g/dL \\ \hline
7 & ABSOLUTE\_LYMPHOCYTE\_COUNT & K/\(\mu\)L \\ \hline
8 & LYMPHOCYTE\_PERCENTAGE & Percentage \\ \hline
9 & NEUTROPHIL\_ABSOUTLE & \\ \hline
10 & PLATELET\_COUNT & K/\(\mu\)L \\ \hline
11 & HIGH\_SENSITIVITY\_TROPONIN T\_2 HOUR & ng/L \\ \hline
12 & TROPONIN I & ng/L \\ \hline
13 & TROPONIN T & ng/L \\ \hline
14 & TROPONIN T\_INTERPRETATION & \\ \hline
15 & DIASTOLIC BP & mmHg \\ \hline
16 & SYESTOLIC BP & mmHg \\ \hline
17 & GLASGOW\_COME\_SCORE & \\ \hline
18 & HEART\_RATE\_MONITORED & bpm \\ \hline
19 & HEIGHT & cm \\ \hline
20 & PAIN SCORE & \\ \hline
21 & PULSE OX & Percentage \\ \hline
22 & PULSE RATE & bpm \\ \hline
23 & RESPIRATORY RATE & bar/min \\ \hline
24 & TEMPRETURE C & Centigrade \\ \hline
25 & WEIGHT\_KILOGRAM & kilogram \\ \hline
26 & BODY\_MASS\_INDEX & kg/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Input variables. The selected features reflect a comprehensive combination of demographic, physiological, and biochemical indicators that are clinically relevant to the early identification and prediction of Acute Aortic Syndrome (AAS). Here is a brief overview of their significance:  
* Age (years): Age is a major risk factor for AAS, as vascular elasticity decreases and aortic wall fragility increases with age. * Gender (M and F): AAS tends to be more common in males, but outcomes may vary by gender, making it a relevant predictive variable. * D-dimer (ng/mL): Elevated D-dimer levels indicate active clot formation and breakdown, often present in AAS due to aortic wall disruption. * HBA1C (%): Reflects long-term glucose control; poorly managed diabetes is associated with vascular damage, which may contribute to aortic pathology. * Hemoglobin (g/dL): Low levels can indicate bleeding or anemia, while high levels may be associated with increased blood viscosity and cardiovascular strain. * Absolute lymphocyte count/lymphocyte percentage: These can reflect immune response or systemic inflammation, which may be indirectly linked to vascular injury. * Neutrophil absolute: High neutrophil counts often reflect inflammation or acute stress response, commonly seen in serious cardiovascular events. * Platelet count (K/uL): Platelets play a role in clotting, and abnormal levels could signal systemic inflammation or coagulopathy relevant to AAS. * Troponins (ng/L): Elevated levels, especially high-sensitivity troponin, indicate myocardial injury, which may occur with or mimic AAS. * Blood pressure (mmHg): Both systolic and diastolic pressures are critical, as hypertension is a key risk factor and exacerbating condition in AAS. * Glasgow Coma Score: While less directly related, altered consciousness could indicate severe hemodynamic compromise.