## 4 Discussion  
The development of ML models for solving practical problems with real-world data requires careful attention to the appropriate selection of hyperparameters to handle specific datasets. In particular, our study highlights the critical role of hyperparameter optimization in ML algorithms trained for predicting breast cancer recurrence. Looking at the optimal combinations of the resulting hyperparameters in Table 2, we can see that for LR, the choice of the'saga' solver and the '11' penalty suggests that L1 regularization is effective in handling high dimensionality and multicollinearity in the data. As for DT, a reduced maximum depth of estimators and limited number of maximum leaf nodes reflect a preference for simpler, shallower models, which are less prone to overfitting and can generalize better on our dataset. In the cases of GB and XGB, a low learning rate and a moderate maximum depth of estimators show that models with slower learning and less depth tend to improve performance, possibly by avoiding overfitting. Finally, for DNN, the choice of a single hidden layer with 100 neurons, together with the Adam optimizer and a low learning rate, turned out to be the most effective, suggesting that even in shallow architectures, it is possible to obtain good performance when they are properly optimized. Not all the models in this study benefited equally from hyperparameter optimization. Simpler models, such as LR, experienced virtually no benefit, suggesting that using their default settings might be a reasonable approach for the task at hand. However, the improvements obtained with hyperparameter optimization become more significant as the models grow in complexity--that is, a greater number of hyperparameters. In our study, the improvement in performance was greater in the more complex models, i.e., GB, XGB, and DNN. For example, the DNN model showed a substantial increase, from 0.72 in recall and 0.76 in F1, using the default values, to 0.92 and 0.91 with the hyperparameter values optimized, respectively. The results also highlight the crucial role that hyperparameter selection has in determining the final performance of the models, as well as hint at the possible consequences of not investing sufficient effort in the optimization phase. Before optimization, one would have chosen to use LR or GB, depending on the metric to maximize. However, after hyperparameter tuning, XGB is clearly positioned as the optimal choice. Therefore, the lack of systematic hyperparameter optimization risks discarding a more efficient and robust algorithm in favor of a simpler one that requires minimal adjustment, but that is incapable of modeling complex non-linear data, as is often the case in the healthcare domain [37]. In the context of predicting breast cancer recurrence, the absence of hyperparameter optimization in prior studies, like [PERSON] et al. [25], [PERSON] et al. [26], and [PERSON] et al. [27], raises concerns about the validity of their conclusions. Hyperparameter optimization could have led to a significantly higher performance for the models used or even revealed a different superior model, one better suited to capturing the intricacies of health data. Our observations add to a growing body of research supporting the importance of hyperparameter optimization in the development of ML models for healthcare. Several previous studies have shown substantial performance gains after implementing this technique [38; 39; 40]. This reinforces our findings that prioritizing hyperparameter optimization can significantly enhance model accuracy and robustness. Therefore, rigorous hyperparameter optimization should be a standard practice in future research to ensure a fair comparison of ML models and fully leverage their capabilities in healthcare. The improvement in the performance of the algorithm selected after optimization has significant implications as it can have profound consequences in decision-making regarding prevention, early detection, and treatment strategies for breast cancer recurrence [41].