Machine Learning Identifies Sociodemographic Cancer Patterns

A systematic review in J Med Internet Res (2026) examined how machine learning is used to detect associations between sociodemographic factors and cancer outcomes. The authors screened 328 records and included 19 studies, finding predominance of supervised methods—particularly random forest and XGBoost—common use of age, sex, education, income, and geographic variables, and reliance on cross-validation with infrequent external validation. The review urges integrating contextual social determinants, improving transparency, and expanding external validation for greater equity and generalizability.