This study presents a web module designed to predict breast cancer risk by integrating clinical and psychological variables. Recognizing that conventional models, such as Gail and Tyrer-Cuzick, primarily use biological and reproductive data, this model incorporates stress-related psychological traits, such as emotional suppression and repression, particularly relevant to the Mexican female population. Clinical and psychological data from 150 women were collected and analyzed using a novel machine learning method: Differential Evolutionary Linear Discriminant Analysis for Feature Extraction (DE-LDA_FE). The results showed that using either clinical or psychological data alone yielded moderate classification accuracy (~62%) and F1-score (~60%). However, combining both types of data significantly increased accuracy, reaching 80.91% and F1-score 81.09%This suggests that psychological factors play a crucial role in early detection and breast cancer risk classification. The web tool has potential applications in public health by offering a risk assessment that is accessible and tailored to Mexican women and can be used especially in low-resource settings where early diagnosis is crucial.