## 4 Discussion
The overall accuracy at this point was 78%, reflecting the proportion of correct classifications (both true positives and true negatives) across all the cases examined. In addition to these metrics, the model's precision at the optimal threshold was 80%, which indicates that 80% of the positive predictions were indeed correct. The F1 score, calculated as the harmonic mean of the precision and recall, was 0.84. This high F1 score demonstrates the balanced performance of the model in terms of the precision and recall, highlighting its strength in identifying true positives while maintaining a reasonable level of false positives. These outcomes indicate the commendable capability of our model to differentiate between women at risk of developing breast cancer, although there is room for improvement in minimizing the false positives. Upon comparing our ML-based model with traditional statistical-based models referenced in the introduction, such as the Gail model, BOADICEA, BRCAPRO, and IBIS, we observed the notable superiority of our ML approach. Traditional models, such as the BOADICEA and BCRAT, have shown an AUC between 0.53 and 0.64 [16; 17; 18; 19; 20]. There is a 36 to 47% chance that these models will not identify high-risk women, while some low-risk women may receive unnecessary preventive treatments [19; 38]. Our analysis underscores how our ML model surpasses these constraints, offering a more accurate and personalized risk prediction, potentially enhancing the identification of women who would benefit most from targeted screening or preventive interventions. Moreover, the inclusion of lifestyle variables, hormonal dynamics, and mutational status in our model emphasizes the importance of considering a broad spectrum of risk factors, beyond those traditionally used in statistical models. Our study offers valuable insights, but it is important to acknowledge certain limitations that should be taken into account when interpreting the findings. Firstly, this study is focused on a single familial cancer center, limiting the generalizability of the findings to broader populations. Secondly, we lack information on diet and physical activity, which play a central role in lifestyle factors and especially in breast cancer [50; 64]. In addition, the exclusion of patients with concomitant tumors or metastases ensures that the study focuses solely on primary breast cancer, which is crucial for understanding the specific risk factors associated with the initial onset of the disease. However, this exclusion may limit the generalizability of the findings to those with more complex cancer histories, where multiple malignancies could interact in ways not captured by this study. Furthermore, by excluding younger and older women, as well as those with other tumors or metastases, the generalizability of the findings is limited. This specific focus provides valuable insights into the breast cancer risk for the selected demographic but may not fully apply to broader populations. Consequently, while our findings are significant for understanding the risk in genetically predisposed women, further studies are needed to explore these factors in more diverse groups. Another significant limitation is the assessment of only one mutation (BRCA genes). Genetic factors are crucial to understanding the cancer risk; however, our work does not delve deeply into this aspect. Not collecting extensive genetic data on a large scale restricts our capacity to thoroughly investigate the relationship between genetic factors and lifestyle influences.