AUTHOR=Casaes Teixeira Bruno , Toporcov Tatiana Natasha , Chiaravalloti-Neto Francisco , Chiavegatto Filho Alexandre Dias Porto 

TITLE=Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach

JOURNAL=International Journal of Public Health

VOLUME=Volume 68 - 2023

YEAR=2023

URL=https://www.ssph-journal.org/journals/international-journal-of-public-health/articles/10.3389/ijph.2023.1604789

DOI=10.3389/ijph.2023.1604789

ISSN=1661-8564

ABSTRACT=Objectives: To test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Methods: Age-standardized CM was extracted from official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the ten most frequent cancer types. Results: Gradient boosting trees algorithm presented the highest coefficient of determination (R2=0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48–96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were percentage of white population and residents with computers. Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality.
Keywords: Machine-Learning, Cancer, Spatial-Clusters, Socioeconomic, Brazil