Conveners
Breast Cancer Risk Prediction: Exploring AI, Federated Learning, and Interpretability Approaches
- Heloisa Barbosa Da Silva (Universidade de Coimbra (PT))
Description
Breast cancer is a major health concern. It is one of the leading causes of cancer related death among women and one of the most commonly diagnosed type of cancer. The risk factors that influence the likelihood of developing this disease are numerous, including modifiable factors such as lifestyle and nutrition. Currently, breast cancer screening tools are tailored to detect the disease rather than to predict it. However, overdiagnosis and overtreatment remains significant concerns. Knowing who is at high risk is extremely important in determining for whom breast cancer screening is most effective and who should be prioritized.
In this project, using The European Prospective Investigation into Cancer and Nutrition – a long-term study with more than half million participants, as the database, several machine learning algorithms, including federated learning, were used to test and to evaluate the most relevant variables that affects the model’s output. From the results insights were found regarding lifestyle and nutritional variables information, for example root vegetables consumption, sweets consumption, alcohol consumption in a specific period of life, that could be useful for new researchers and studies.