## 4 Results
### Weekend and Holidays Model Generation
It was concluded that the most efficient algorithm in the case of working days was the GLM, whereas in the case of public holidays it was the SVM algorithm with a linear kernel. It was shown that there was a direct relationship between environmental factors and the influx of patients to the health services in Jaen and this relationship was statistically quantified with regression coefficients. The analysis of these coefficients allowed a better understanding of the logic of the functioning of the health services. In terms of the analysis of the model with respect to the various months, it was seen that weekdays in the months of July, August and September corresponded with negative coefficients. Whereas, the months of January and February had the highest positive coefficients. This situation is due to the fact that these are the coldest months in Jaen and usually coincide with the emergence of common diseases such as colds and flu. Air quality was observed to have a positive weight of 0.03 for the days with bad air quality and \(-0.03\) for days with good air quality. Finally, weather attributes also had an important weight in the model, reaching the highest weights of maximum, minimum and mean temperatures, and relative humidity. As expected, it was confirmed that the selected algorithms performed very well when there was high temporality in the sample data. When the sample data size was small SVM performed better, as is the case for the prediction for weekends and holidays. In order to confirm these results, they were contrasted with two other regression algorithms widely used in health prediction, Random Forest and eXtreme Gradient Boosting (XGBoost) [32; 33; 34; 35]. The theoretical accuracy of both models determined the difference between the predicted \begin{table}
\begin{tabular}{c c c} \hline \hline
**AttributeFinal del Formulario** & **Value** & **Coefficient** \\ \hline Intercept & & 0.49863636 \\ day of week & MONDAY & 0.18892462 \\ day of week & TUESDAY & \(-0.15558891\) \\ day of week & SATURDAY & 0.10970659 \\ day of week & WEDNESDAY & \(-0.09673843\) \\ day of week & FRIDAY & \(-0.07478812\) \\ day of week & THURSDAY & 0.02517979 \\ day of week & SUNDAY & 0.00330445 \\ relative wet & & \(-0.01722341\) \\ month & 8 & \(-0.28521676\) \\ month & 7 & \(-0.24222796\) \\ month & 2 & \(0.18287157\) \\ month & 1 & \(0.14417783\) \\ month & 3 & \(0.12638826\) \\ month & 9 & \(-0.10298506\) \\ month & 12 & \(0.07631651\) \\ month & 11 & \(0.04979944\) \\ month & 10 & \(0.03127474\) \\ month & 5 & \(0.01690143\) \\ month & 4 & \(0\) \\ month & 6 & \(0\) \\ air quality & 1 & \(0.03298887\) \\ air quality & 0 & \(-0.03298887\) \\ maximum temperature & & \(0.06454707\) \\ average temperature & & \(0.01384894\) \\ minimum temperature & & \(0.04531514\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Coefficient statistics for linear regression of the predictive model. number of patients attending the health centre and the actual data. For the weekday cohort, the results were: In the case of the Random Forest algorithm an absolute error of 6.23% was obtained, while with the XGBoost algorithm 6% was obtained. It was confirmed that the three selected algorithms offered a higher predictive accuracy. As seen above, the GLM gave the best result with an absolute error of 2.11% compared to the 2.31% error of the SVM with a linear kernel or the 4.22% error of the SVM with a Gaussian kernel. A visual analysis of the results of the Random Forest and XGBoost algorithms is shown in Figure 11, where it can be seen that GLM fits the predictions in all months of the year. At the application level, the decision support system has a simple, user-friendly interface so it can be used by primary care resource managers. The decision support system can be accessed from any web browser and can be used from a PC or a mobile device. The system basically consists of a form to insert data and another final screen where the results are displayed.