## 4 Results
### Weekend and Holidays Model Generation  
In the case of the cohort of holidays and weekends the regression models were generated using each of the algorithms used for the cohort of days of the week. However, the results were different, for these data the algorithm that statistically best fitted the prediction  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Principio del** \\ **Formulario** \\ **Attribute Final del** \\ **Formulario** \\ \end{tabular}  & \begin{tabular}{c} **Value** \\ **of the Coefficient** \\ \end{tabular}  & \begin{tabular}{c} **Standardized** \\ **Estimate** \\ **Estimate** \\ \end{tabular}  & \ \begin{tabular}{c} **Linear** \\ **Coefficient** \\ \end{tabular}  & \begin{tabular}{c} **Limit** \\ **Lower** \\ **Coefficient** \\ \end{tabular}  \\ \hline Intercept & & \(0\) & \(1689.13\) & \(1205.85\) & \(2172.41\) \\ Day of Week & Friday & \(-0.432\) & \(-469.539\) & \(-554.58\) & \(-384.503\) \\ day of week & Thursday & \(-0.173\) & \(-192.036\) & \(-277.634\) & \(-106.439\) \\ day of week & Monday & \(0.108\) & \(118.722\) & \(35.165\) & \(202.279\) \\ day of week & Wednesday & \(-0.093\) & \(-105.912\) & \(-193.92\) & \(-17.903\) \\ relative wet & & \(0.115\) & \(2.937\) & \(0.078\) & \(5.795\) \\ month & \(8\) & \(-0.61\) & \(-891.463\) & \(-1025.33\) & \(-757.594\) \\ month  
was SVM with a linear kernel with an absolute error of 12.77, Table 6. This table shows the results of the statistical and theoretical parameters of the application of the algorithms for the holiday data cohort. Theoretically the smallest mean square error occurred with the SVM algorithm with a linear kernel. This result was logical since the volume of training data used with this cohort was smaller than in the case of the weekday cohort. Then, the theoretical accuracy of the model was analysed by determining the extent to which the predicted values corresponded to reality. Similarly, in the case of the cohort of weekday values predictions were compared with the actual values of patients on holiday in 2018. In this case, it was confirmed that SVM with a linear kernel was the best algorithm with an absolute error of 7.6% compared with 9.43% of the error of the GLM or 13.60% for the SVM with a Gaussian kernel. A visual analysis of these results is shown in Figure 10 which shows how both functions are adjusted. It was observed that in some months both functions matched almost exactly while other months they did not, but the trend of both was adjusted with high precision. The coefficient statistics for linear regression in Table 7 was checked, facing the same external factors, and this model had a different behaviour with respect to the previous cohort of weekdays. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Predictive** & **Mean Absolute** & **Mean Actual** & **Mean** & **Root Mean** \\  & **Confidence** & **Error** & **Value** & **Predictive Value** & **Square Error** \\ \hline SVM with & & & & & \\ Gaussian Kernel & 2.91\% & 26.27 & 132.34 & 132.28 & 34.78 \\ SVM with Linear & 39.04\% & 12.77 & 132.34 & 130.66 & 21.81 \\ Kernel & 35.53\% & 15.72 & 132.34 & 131.3 & 23.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the main metrics after generating models for the cohort of holidays. Figure 10: Comparison of the predictions of the holidays with respect to the actual data of 2018. In an analysis of data predicted for the cohort of the weekdays it was determined that Monday was the day more patients requested the emergency services, with a coefficient of 0.188. This fact seems logical since on Saturday and Sunday primary care services are not available and therefore many patients on Monday are forced to go to the emergency department for pathologies that worsened during the weekend. On the days of the weekend, it was detected that on Saturday there was a greater coefficient value than on Sunday. This may be because on Sunday many sick users prefer an appointment scheduled for Monday in primary care.