## 1 Introduction
These limitations highlight the need for more adaptable and inclusive models that integrate a wider array of genetic, environmental, and lifestyle factors to enhance the breast cancer risk prediction accuracy. In conclusion, although traditional statistical tools are widely used for identifying target populations for screening, they often show weak correlations with actual screening outcomes. More advanced methods, such as machine learning (ML) algorithms trained on the same data, may offer superior predictive capacity [38], as suggested by successful ML applications in various domains, underscoring the potential of these methods to enhance the predictive accuracy across different areas, including healthcare [39]. Several factors explain why ML models may outperform traditional statistical models. Firstly, ML handles complex, non-linear relationships by incorporating a vast range of risk factors (genetic, lifestyle, and environmental), without oversimplifying their relationships. Algorithms like random forests or neural networks can model complex non-linear interactions between variables, which traditional models often miss. For example, [40] demonstrated that ML models, due to their inherent non-linearity, achieved equivalent (and superior) performance across different ethnicities compared with the Tyrer-Cuzick model, which showed significant differences in the AUC values for white and African American women. The same paper also showed another strong point of ML models, i.e., the capability of incorporating high-dimensional data, such as extensive genomic information, providing a more comprehensive risk prediction. Another advantage of ML is the ability to address data completeness issues [41]: ML models are better equipped to handle missing or incomplete data through advanced imputation techniques, by the definition of dummy variables, and by architectures natively capable of facing missing data issues, such as decision-tree-based approaches. This makes ML models more robust in real-world settings where perfect data are rarely available. ML can also be continuously updated as new data are collected,ensuring they remain accurate or improve over time and adapt to changes in population health dynamics [42]. Additionally, ML can work with unstructured data, such as images, text, or audio, and deep learning (DL) methods can automatically identify and construct features from raw data, reducing the need for manual feature extraction and selection. Overall, ML models and other artificial intelligence (AI) approaches may indeed show significant potential for improving the accuracy of classifying women with and without breast cancer. In this scenario, the aim of our study was firstly to thoroughly examine the differences between healthy subjects and those with diagnosed breast cancer, all with a family history for cancer. Similar to traditional statistical models, we focused on lifestyle factors such as the BMI, voluntary habits like smoking, hormonal influences and the mutational status of the BRCA genes. This approach allowed for the collection and utilization of the majority of predictive variables known to increase the risk of developing breast cancer. Secondly, utilizing the capabilities of AI, risk factors served [42] as features to predict subjects at high risk of breast cancer. Unlike traditional statistical models, the proposed model harnesses more advanced predictive models in the AI domain, enabling increasingly personalized preventive interventions and treatments.