## 3 Results and Discussion  
The analysis of our proposal, which includes the development of an MVP (Minimum Viable Product), was carried out at San Juan de Dios Hospital (Seville), specifically in the Early Care unit, which treats children up to 6 years of age, aiming to work on patient skills in order to acquire the skills required for integration into society. For this, the models described in Section 2.4 were trained taking into consideration the 420 files in the hospital. The goodness of fit, summarized in Table 2, was calculated comparing the treatment automatically proposed by the algorithm with that proposed by the expert, carrying out an 80-20 division of test-training. It should be noted that the field \"VN\" states the number of explanatory variables used in each individual model. As explained in Section 2.3, the initial number of variables chosen was 40. However, 10 and 9 were used for the CHAID and C5 methods, respectively, as the other variables did not add value to the result. Based on the precision of the different approaches, it can be observed that XGBOOST displays the best behavior, with a precision value of up to 86.45. Thus, it can be stated that the analysis of 420 files resulted in 370 correct diagnoses and 58 erroneous ones. Due to the nature of the study, where it is important to prevent an excessive concentration of errors in some diagnoses, the XGBOOST confusion matrix is presented in Table 3. Here, the different diagnoses (processed) are coded from I to IV following the description made in Section 2.1. According to the values presented in the aforementioned section, it can be observed that the errors are not imbalanced. Some of the errors of the classification \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Precision** & **VN** \\ \hline XGBoost & 86.45 & 40 \\ Random Forest & 79.44 & 40 \\ Linear Regression & 73.83 & 40 \\ LSVM & 68.46 & 40 \\ C5 & 61.45 & 9 \\ CHAID & 68.46 & 10 \\ NEURAL NETWORK & 48.131 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Precision of the AI algorithms considered. system are confusions between a cognitive disorder and a language and communication disorder, as even for experienced specialist physicians, it can be difficult to distinguish between the two at certain ages. Finally, Figure 1 shows the system gain. _Gains_ are defined as the proportion total hits that occur in each quantile or increment. That is:  
\[Gains=\frac{\text{number of hits in quantile}}{\text{total number of hits}}\times 100\]  
Notice that our graph can only show results for one categorical value at a time: in our case, processes that were correctly identified. The lines in the figure represent the following:  
* The thick diagonal red line is the at-chance model. * The blue line is the perfect classifier. * The green line in the middle is our current model. * The area under the green curve represents how well our system is behaving. In the figures, the Y-axis represents the cumulative percent of hits or the gained, so for example, at value 20, we have found 20% of the category correctly identified for a certain category, at value 60, we have found 60%, and so on. The X-axis represents the percentile groups ordered by confidence. Taking a closer look at the perfect prediction line, we can see that, for example, in the sensory-motor process, once we have gone through approx. 20% of the data, the model correctly predicts 100% of the time. If we take a look at the distribution (see Table 4), this corresponds to the distribution of sensory-motor cases. The area under the green line is known as \"under the curve\", which indicates how much better our model is than the at-chance model, and the area under the blue line indicates where our model can be improved, as our goal is to be as close to the perfect prediction model as possible. Figure 1: Gain of our artificial intelligent system (AI) against at-chance model (AG). Specialist diagnostic (SP) for the different processes.