AI-Driven Biopsychosocial Screening for Breast Cancer: Enhancing Risk Prediction via Differential Evolutionary Linear Discriminant Analysis for Feature Extraction

In Mexico, the high prevalence and mortality rates associated with breast cancer (BC) constitute a critical public health challenge that demands context-specific preventive measures. This study proposes an integrative framework for predicting BC risk based on a biopsychosocial model. We hypothesize that emotional suppression and repression act as key neuroendocrine disruptors and predisposing factors within the Mexican female population. To test this, we systematically compared the predictive performance of various machine learning classification models using the clinical, psychological, and combined profiles of 110 women. These models were evaluated with and without the application of a robust evolutionary algorithm: Differential Evolutionary Linear Discriminant Analysis for Feature Extraction (DE−LDAFE). The results demonstrated that integrating clinical and psychological data into a combined latent space significantly improved the performance of the classification algorithms. The Artificial Neural Network achieved the highest metrics (0.9975 Precision; 0.9976 F1-score). However, due to the inherent “black-box” nature of these models (limited clinical interpretability), the Decision Tree emerged as the optimal practical alternative, providing highly competitive (0.8874 Precision; 0.8853 F1-score) and interpretable results. These findings provide empirical evidence that psychological factors, rather than being mere incidental comorbidities, could be associated with the etiology of breast cancer and be used as risk factors in predicting the disease. Ultimately, this AI-driven biopsychosocial screening model offers a scalable, low-cost, and context-adapted risk assessment tool for early BC diagnosis in Mexican women.