Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features

This study investigates the interplay among social demographics, built environment characteristics, and environmental hazard exposure features in determining community level cancer prevalence.

Utilizing data from five Metropolitan Statistical Areas in the United States: Chicago, Dallas, Houston, Los Angeles, and New York, the study implemented an XGBoost machine learning model to predict the extent of cancer prevalence and evaluate the importance of different features.

Our model demonstrates reliable performance, with results indicating that age, minority status, and population density are among the most influential factors in cancer prevalence.

We further explore urban development and design strategies that could mitigate cancer prevalence, focusing on green space, developed areas, and total emissions.

Through a series of experimental evaluations based on causal inference, the results show that increasing green space and reducing developed areas and total emissions could alleviate cancer prevalence.

The study and findings contribute to a better understanding of the interplay among urban features and community health and also show the value of interpretable machine learning models for integrated urban design to promote public health.

The findings also provide actionable insights for urban planning and design, emphasizing the need for a multifaceted approach to addressing urban health disparities through integrated urban design strategies.