## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phases** & **Classifiers** & **Dts\_1** & **Dts\_2** & **Dts\_3** \\ \hline \multirow{5}{*}{Phase 1} & SVM & 0.001 s & 0.003 s & 0.003 s \\  & KNN & 0.002 s & 0.003 s & 0.002 s \\ \cline{1-1}  & LDA & 0.004 s & 0.004 s & 0.003 s \\ \cline{1-1}  & XGBoost & 1.972 s & 0.92 s & 1.00 s \\ \cline{1-1}  & Random Forest & 0.013 s & 0.011 s & 0.011 s \\ \hline \multirow{5}{*}{Phase 2} & SVM & 0.0019 s & 0.002 s & 0.001 s \\  & KNN & 0.0019 s & 0.002 s & 0.0019 s \\ \cline{1-1}  & LDA & 0.0049 s & 0.002 s & 0.002 s \\ \cline{1-1}  & XGBoost & 1.09 s & 0.912 s & 0.91 s \\ \cline{1-1}  & Random Forest & 0.011 s & 0.011 s & 0.011 s \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Summary of the achieved time results for all binary classifiers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Dts\_1** & **Dts\_2** & **Dts\_3** \\ \hline Multi-class SVM & 0.003 s & 0.002 s & 0.001 s \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Training and testing results for multi-class SVM. risk alert because of its Sys value that belonged to Class 1. Finally, the tuple [75.53; 55.49] returned a critical risk alert given that both Sys and Dias values belonged to Class 2 and Class 1 respectively and therefore the tuple would be considered as a Class 2 alert according to the rules underlying our approach, which automatically classified the tuple in the highest class of the two child classes. Table 21 depicts those rules based on our introduced logical operator \(\mathcal{Z}\)(where S, R and C represent normal, Class 1 and Class 2 respectively. Figure 16 summarizes our approach from data collection, through the discovery of the learning model based on the two classification processes, to the final model used to precisely classify any abnormality that might present a high or a critical risk for the patient. So, this research work responded perfectly to our concern about proposing an intelligent system that was specific to each patient and about avoiding knowledge based on generalized thresholds. That being said, our study offered a detection learning model adapted to a single patient on a case-by-case \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Tuple** & **Values** & **Decision** & **Normalization** & **Mean** & **Std** & **Class** & **Alert** \\ \hline \multirow{2}{*}{[123.19, 78.63]} & 123.19 & Abnormal & 1.67 & 110 & 10 & 1 & [Sys,Dias] \(\in\) Class 1 \\  & 78.63 & Normal & - & 73 & 6 & - & High risk \\ \hline \multirow{2}{*}{[93.02, 67.65]} & 93.02 & Normal & - & 110 & 10 & - & No risk \\  & 67.65 & Normal & - & 73 & 6 & - & \\ \hline \multirow{2}{*}{[107.87, 84.57]} & 107.87 & Abnormal & - & 107 & 7 & - & [Sys,Dias] \(\in\) Class 1 \\  & 84.57 & Normal & 1.51 & 74 & 7 & 1 & High risk \\ \hline \multirow{2}{*}{[108.77, 59.66]} & 108.77 & Normal & - & 107 & 7 & - & [Sys,Dias] \(\in\) Class 2 \\  & 59.66 & Abnormal & -2.04 & 74 & 7 & 2 & High risk \\ \hline \multirow{2}{*}{[110.73, 67.70]} & 110.73 & Normal & - & 98 & 11 & - & No risk \\  & 67.70 & Normal & - & 67 & 8 & - & \\ \hline \multirow{2}{*}{[75.53, 55.49]} & 75.53 & Abnormal & -2.04 & 98 & 11 & 2 & [Sys,Dias] \(\in\) Class 2 \\  & 55.49 & Abnormal & -1.43 & 67 & 8 & 1 & Critical risk \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Risk classification truth table. Figure 16: Illustration of the general process of designing our model. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathcal{Z}\)K & **S** & **R** & **C** \\ \hline S & - & C1 & C2 \\ R & C1 & C1 & C2 \\ C & C2 & C2 & C2 \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Simulation of the manipulations carried out using several measurements. basis. Future works will focus on modeling processes using techniques to optimize the active search for intervals and boundary zones related to the achieved results using the normal distribution. This would allow a deeper learning of the patient's habits and thus adapt a given medical treatment.