## 4 Discussion
Significant differences were also observed in relation to hormonal factors, including the age at menopause, PMA and use of hormonal replacement therapy. These results are in line with the literature, including the prolonged exposure to endogenous hormones in women with a later onset of menopause [56]. Surprisingly, the affected group reported a higher likelihood of breastfeeding compared to the healthy subjects. This result does not align with research suggesting the protective effects of full-term pregnancies and breastfeeding against breast cancer [57; 58; 59; 60; 61]. It is possible that this is a consequence of the higher number of children among the women in Group B compared to Group A. A history of medical abortions, along with the use of multiple contraceptive methods, has been associated with an increased risk of postmenopausal breast cancer [59]. Interestingly, women who have used intrauterine devices for contraception for over twenty years tend to have a lower likelihood of developing breast cancer compared to others in their age group [59]. However, research consistently shows that oral contraceptives can significantly elevate the breast tumor risk. Despite this increased risk, it is essential to recognize the benefits that oral contraceptives provide, which must be weighed against their potential risks [60]. The significant disparity in hormonal replacement therapy (HRT) usage between the two groups highlights the ongoing debate around HRT and the breast cancer risk. With the affected group more likely to have used HRT, this finding aligns with the literature suggesting a potential association between HRT, especially combined estrogen-progestogen therapies, and an increased breast cancer risk [62; 63]. Lastly, the observed differences in the BRCA mutation statuses between Group A and Group B underline the critical role of genetic factors in the breast cancer risk. It is normal to observe a higher number of mutations among healthy patients, since for each affected patient, one or more healthy blood relatives are sent for genetic testing to determine their risk. The presence of VUSs in a larger portion of the affected group also highlights the challenges in genetic testing and interpretation, emphasizing the complexity of genetic contributions to breast cancer and the need for further research in this area. By using factors that significantly differed between Group A and Group B as features, we trained different ML models that were able to predict subjects at an increased risk of developing a breast tumor. The best performer was the ensemble of decision trees. To assess the stability and reliability of the model's performance, we conducted a 10-fold cross-validation procedure and analyzed the distribution of the AUC values across each fold. As shown in Figure 2, the AUC values consistently ranged between approximately 0.78 and 0.88, with minor fluctuations. This consistency across the different folds indicates that the model's ability to discriminate between those at risk and those not at risk of developing breast cancer is robust and not overly dependent on any particular subset of the data. Building on these cross-validation results, the model achieved an overall AUC of 81%. An AUC of 0.5 indicates no discrimination ability, equivalent to random guessing, while an AUC of 1.0 represents a perfect classifier. In our model, the AUC of 81% suggestsa strong ability to distinguish between individuals with and without the risk of developing breast cancer. To identify the optimal cutoff point, we analyzed the ROC curve to find the threshold that maximizes the classifier's accuracy. This optimal point balances the trade-off between sensitivity and specificity, aiming to maximize the model's effectiveness in correctly identifying true positives while minimizing false positives. At this threshold, the model achieved a sensitivity of 88%, meaning it correctly identified 88% of those at risk, and a specificity of 57%, indicating its moderate ability to correctly identify those not at risk.