Skin cancer constitutes the most frequent neoplasm in Brazil, and its potential for invasion is shaped by several elements, among which geographical factors play a notable role. Recognizing these elements is essential for advancing diagnostic accuracy and therapeutic effectiveness. This investigation examined how geographical region influences skin cancer invasiveness in Brazil by identifying distinct predictive patterns associated with different regions. Data obtained from the Hospital Cancer Registries (RHC) maintained by Brazil’s National Cancer Institute (INCA) underwent thorough analysis and preprocessing. Machine learning algorithms were then applied, and model interpretations were generated using the SHapley Additive exPlanations (SHAP) technique. The analysis indicated that geographical location is a key determinant in forecasting skin cancer invasiveness, underscoring the need to incorporate regional factors in future research. This study established that regional characteristics within Brazil significantly affect predictions of skin cancer invasiveness. While challenges such as data imbalance were encountered, the outcomes provide valuable insights for crafting more targeted policies to combat skin cancer across the country.
