## 1 Introduction
The aim of this work was to predict the number of medical attendances at a primary care centre each day by using various regression algorithms. The process takes as its starting point the hypothesis that there are several variables related to the target variable, e.g., patient attendance at the health centre, the history of medical care provided each day and a series of meteorological variables. In this sense, as described above, there are several studies that corroborate this relationship. For all these reasons, data mining techniques are suitable for this type of analysis [22; 23; 24; 25; 26], to generate a predictive model capable of predicting in advance the number of patients attending each day. In this case, a supervised learning study was used where all the predictor variables were labelled. This information is key to the proper management of resources and a decision support system will be developed to facilitate its use by healthcare managers. In short, this work has of main objective of (1) generating a prediction model of the number of healthcare services that will be demanded daily, and as a complement to the previous objective: (2) to nest this data mining model in an application to improve the convenience, accessibility and use of the proposed software by end users, who are the managers of the health centre. Several regression algorithms can be applied, such as linear regression, logistic regression, generalized regression model, one-class support vector machine (SVM), etc. In this study, a priori the nature of the variables is unknown and little training data is available (daily health care data over the last five years were available); therefore, linear and non-linear linear regression algorithms were used. The first algorithm selected In this study was the generalized linear model (GLM), which works mathematically as the weighted sum of the features with the mean value of the distribution assumed using the link function g, which can be chosen flexibly depending on the type of result. \[\mathrm{g}(\mathrm{EY}(\mathrm{y}|\mathrm{x})=\beta_{0}+\beta_{1}\mathrm{x}_{1 }+\cdots\beta_{\mathrm{p}}\mathrm{x}_{\mathrm{p}} \tag{1}\]  
Another linear algorithm selected was SVM, which has the advantage of being able to be used with different kernels. These allow the data to be distributed on a hyperplane according to a function, which facilitates the adaptation of the algorithm to the nature  
there was a limited set of training data, since authors had a limited number of years with daily data of medical attendances in the health centre and few meteorological variables were needed. The nonlinear algorithm applied was the SVM algorithm with a Gaussian kernel. This kernel applies the following transformations to the data:  
\[\mathrm{K}\big{(}\mathrm{x},\mathrm{x}^{\prime}\big{)}=\exp\big{(}-\gamma\| \mathrm{x}-\mathrm{x}^{\prime}\|2\big{)} \tag{3}\]  
The value of \(\gamma\) controls the behaviour of the kernel. If it is very small, the final model is equivalent to that obtained with a linear kernel, but if the value increases the data becomes more distant, forming a Gaussian bell in the hyperplane, adapting very well when the data nature has no linear distribution. In summary, these are the properties of the three algorithms applied in this study based on the hypothesis of a priori ignorance of the relationship between the variables considered and the target attribute, and also considering that the training data available was limited and the predictor variables were few in number. Thus, the complexity of these algorithms means that the relationship between the attributes used cannot be described by a specific equation. **New Contribution**  
The number of patients coming to the health centre varies greatly from day to day depending on the month of the year. The main factors influencing this are weather and environmental factors. Nowadays, meteorological systems provide us with weather information in advance.