## 4 Results
### Weekday Model Generation  
The regression models were generated from the algorithms already described in the previous section. For the cohort of weekday, the rain attribute has been dismissed as non-influential on the target. The results obtained are shown in Table 4 where the precision of models generated from each algorithm is shown. These results concluded that the algorithm theoretically best modelled to our issue was the GLM, with a predictive confidence of 85.17% and an absolute error of 49.09. Then, the three models were validated by comparing their predictions with the actual data of 2018. In order to perform this validation, the absolute error of the model was calculated, considering this as the difference between the predicted data and the actual data. The GLM algorithm results had an error of the 2.11% versus the 2.31% of the SVM with a linear kernel and 4.22% for the SVM with a Gaussian kernel. Figure 9 shows the distribution of data making the prediction model with the actual data. The similarity between both distributions is very significant. The good predictive ability of the GML algorithm to predict the number of patients attending the health centre on weekends and holidays was confirmed. The graph shows a generally good fit despite the large variation in the number of visits between the winter and summer months. However, a more detailed analysis of the graph showed a greater disparity in the month of October and another between the months of March and April. This may be due to the fact that the flu vaccination campaign usually starts in October and this factor was not taken into account directly as another factor in the training data. As for the months of March and April, the period for prescribing medication to treat the symptoms of spring allergies usually begins here, a fact that was not been explicitly considered as a factor, although it was indirectly included in this increase in the number of visits to the health centre. The behaviour of the model against the action of the different factors that influence it was determined by the coefficient statistics for linear regression. This allowed us to understand what is called the logic of the model. In multivariate linear regression, the regression parameters are often referred to as coefficients. When a multivariate linear regression model is built, the algorithm computes a coefficient for each of the predictors  
Figure 9: Comparison of the predictions of the weekdays with respect to the actual data of 2018.  
used by the model. The coefficient is a measure of the impact of the predictor x on the target y. Numerous statistics are available for analysing the regression coefficients to evaluate how well the regression line fits the data. An analysis of the data for days of the week can be seen in Table 5, where the amount of patients attended on Wednesdays, Thursdays, and Fridays, and the amount of returning patients to health centres on Monday added up to a large number of patients. This is because during the weekend the health centre only provides emergency services and if a patient is ill over the weekend and the illness is not serious, they are advised to go to the health centre on Monday. In this sense, an analysis of the attribute of the month detected that during the months of July, August and September, the number of patients decreased. Logically this is because the holiday period and high temperatures coincide. This leads to an apparent absence of pathologies. On the other hand, February and October are the months with the highest number of patients admitted to health centres. Considering the environmental data, the important weight that the maximum, minimum and average temperature have in the model was confirmed, with a standard coefficients estimated of \(-0.175\), \(0.752\) and \(-0.168\), respectively. In terms of relative humidity is was proved to be the least significant meteorological attribute. Finally, the quality of air was the element of the model with least influence, with a coefficient statistic estimated of \(0.068\).