## 4 Results and Discussion  
This section presents the results obtained by the models generated by each of the selected algorithms compared with the application of the GA. Table 5 presents the AUC results for each tested machine learning algorithm without hyperparameter optimization and for the grid search (GRID) and GA approaches. As can be seen from Table 5, the best AUC results were produced by the GA approach with a mean of 0.8454 and median of 0.8498. GA also produced the lowest AUC standard deviation (0.0637) among all tested approaches. Figure 6 helps visualize the performance of the models for the 50 weeks of the course. To confirm the research hypothesis in this work (RQ1 and RQ2), two tests of statistical significance were applied. The objective of the tests was to verify if there was a significant difference in the treatments applied and, if so, which method was the most accurate. The central idea involved in the process of statistical significance is to test whether one treatment, in this study GA, presents a significant result concerning the others [51]. The results had a normal distribution, so analysis of variance (ANOVA) was chosen to verify the existence of a significant difference, and [PERSON]'s test to determine in which treatment it occurred. For this, the \(p\)-value was set to 0.05; thus, values lower than this indicated that the treatment was significant and higher than not significant. In ANOVA, the \(p\)-value was 0.0006865, which reflects the existence of significant differences between the approaches. In [PERSON]'s test, the results produced a \(p\)-value of 0.0475 for the GridSearch and 0.0003 for standard RF, indicating a statistically significant difference between the performance. Thus, statistically, the results obtained by GA were superior to the other treatments. The results obtained from the three approaches are presented in Figure 6. The GA achieved excellent discrimination (AUC > 0.7) as early as week 4. This held until week 24, where the GA  
Figure 6: AUC results for each tested technique during the 50 weeks of the course. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach or** & **Hyperparameter** & **AUC Mean** & **AUC Median** & **AUC Standard Deviation** \\
**Algorithm** & & & & \\ \hline GA & Yes & 0.8454 & 0.8498 & 0,0637 \\ \hline GRID & & 0.7939 & 0.8288 & 0.1056 \\ \hline ADA & & 0.7509 & 0.8062 & 0.1342 \\ \hline DT & & 0.6771 & 0.7065 & 0.1008 \\ \hline LG & & 0.6943 & 0.7198 & 0.1110 \\ \hline MLP & & 0.7353 & 0.7946 & 0.1277 \\ \hline RF & & 0.7752 & 0.8243 & 0.1150 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AUC for 50 weeks. provides outstanding discrimination (AUC \(>\) 0.9). The other approaches still yielded acceptable discrimination results (AUC \(<\) 0.7) until week 22. However, from week 30, the performance of the GA considerably decreased, with the other approaches progressing. One of the factors determining this drop in GA performance was the increase in the number of input attributes. In this situation, the GA tends to find a local solution quickly and converge on it. However, this solution is probably a plateau and the GA getting stuck. This is a problem specific to genetic algorithms that does not occur in the other approaches tested. In the proposed algorithm, the reinsertion step tries to soften this, but as verified from weeks 32 to 42, the GA is still susceptible to this failure. However, the GA was considerably better in early prediction and with limited data. This demonstrates that the refinement of the GA is essential for tuning hyperparameters. Table 6 presents the best configuration obtained by the GA approach for week 25 of the course (individual 37, fourth epoch), with an MLP with an AUC of 91.54, in comparison with the configuration for the same algorithm without hyperparameter optimization. From the first weeks of the courses, satisfactory results were already produced in the prediction of students at risk of dropout.