## 3 Dataset Description  
Our structured dataset has been collected from two to three hospitals located in Mosul city, and the output classes have been verified by more than three specialist medical doctors. The dataset consists of 750 information reports of cardiac patients, and each record has 13 features of which 10 are considered risk factors that represent the most important input features, listed in Table 1 under medical history and examination. The first five rows of Table 1 describe the selected features of our collected dataset. As shown, the type of these features can be either continuous or nominal. Features that deal with Boolean expressions (false/true) are considered nominal while the others are continuous variables. The dataset was classified as stable, unstable non-critical, and unstable critical cases. These classes were collected in a way that creates a balance among the output labels.  
To analyze the dataset, first, preprocessing is conducted to convert the ten most popular risk factors into five vital signs. This conversion process is carried out using a clinical decision-making comprehensive guide [13; 40] to ensure excellent delivery of efficiency and safe medical care. These crucial vital signs can greatly aid medical doctors in rapidly delivering the necessary medical therapy. Hence the preprocessing for feature selection has been handled carefully. This is due to crucial information related to the modeling of the problem possibly being discarded, leading to degrading the system's overall accuracy ([PERSON] et al., 2018).  
Table 2 outlines the necessary sensors and communication components for the proposed monitoring system for cardiac patients. These sensors are crucial for effective implementation of the system, while Table 1 presents the data volume from the sensors to illustrate the feasibility and realization of the system. It is important to note that the data types can be either continuous or categorical. Lastly, Table 3 lists the statistical summary of the collected dataset samples. These features include cardiac disease such as age, BMI, chest pain, shortness of breath, smoking, and so on, which mainly causes the hospitalization of cardiac patients.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Risk Factors and Extracted Features**} \\ \multicolumn{2}{c}{**Medical History**} & \multicolumn{2}{c}{**Medical Examination**} \\ \hline Features & Type & Features & Type \\ \hline Age & Continuous & Heart Rate & Continuous \\ \hline Body Mass & Continuous & Respiratory Rate & Continuous \\ Index (BMI) & & \\ \hline Chest Pain & \multirow{2}{*}{Nominal (0 \(\rightarrow\) NO ; 1 \(\rightarrow\) YES)} & \multirow{2}{*}{SPO2} & \multirow{2}{*}{Continuous} \\ (CP) & & & \\ \hline Shortness of & \multirow{2}{*}{Nominal (0 \(\rightarrow\) NO ; 1 \(\rightarrow\) YES)} & \multirow{2}{*}{Blood Pressure} & \multirow{2}{*}{Continuous} \\ Breath (SoB) & & & \\ \hline Smoking & \multirow{2}{*}{Nominal (0 \(\rightarrow\) NO ; 1 \(\rightarrow\) YES)} & \multirow{2}{*}{Glasgow Coma Scale} & \multirow{2}{*}{Continuous} \\ \hline \multicolumn{4}{c}{Possible Labels and Required Medical Therapy} \\ Health Status (Labels) & & & Medical Therapy \\ \hline Stable & \multicolumn{2}{c}{Stay Home (SH)} \\ \hline Unstable Non-critical & \multicolumn{2}{c}{Emergency Room (ER)} \\ \hline Unstable Critical & \multicolumn{2}{c}{Intensive Care Unit (ICU)} \\ \hline \hline \end{tabular}
\end{table}
Table 1: A cardiac patient’s risk factors (features), health status, and required therapy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **Applicable Sensors** \\ \hline Gender, Age, and Smoking & User Interface (input) \\ \hline Body Mass Index (BMI) & Weight Sensor (e.g., load cell), \\ Height Sensor (e.g., ultrasonic/infrared) \\ \hline Chest Pain & User Interface (input) \\ \hline Shortness of Breath & Piezoelectric Belt or Pulse Oximeter \\ \hline \hline \end{tabular}
\end{table}
Table 2: The required sensors and input data categories for practical cardiac system realization.