Prediction of skin cancer invasiveness: A comparative study among the regions of Brazil

Abstract

ContextSkin cancer is the most incident neoplasia in Brazil, and their invasiveness can be impacted by various factors, including geographical aspects. Identifying these factors is important for improving diagnosis and treatment. ObjectiveThe research focused on analyzing the impact of region on the invasiveness of skin cancer in Brazil, through the identification of regional predictive patterns. MethodsAn analysis and processing of data from the Hospital Cancer Registries (RHC) of Brazil's National Cancer Institute (INCA) were conducted, followed by the application of machine learning algorithms. The SHapley Additive exPlanations (SHAP) approach was employed to provide explanations for the developed artificial intelligence models. ResultsIt was revealed that geography plays a significant role in predicting the invasiveness of skin cancer, reinforcing the need to consider regional specificities in future studies. ConclusionsThe study identified that regional characteristics of Brazil impacts the prediction of the invasiveness of skin cancer. Despite limitations, such as the issue of data imbalance, the findings are important for developing more effective policies in the fight against skin cancer in the Brazil.