用机器学习分析美国乳腺癌筛查差距，发现种族、医疗资源和教育水平影响显著
Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer Screening Among Populations in the United States: Machine Learning Approach
- 基于7万多普查区数据，用随机森林模型分析社会因素对筛查率的影响
- 模型准确率达64.53%，发现黑人比例高、医院多、学历高地区筛查率更高
- 揭示东西部筛查率高于中西部，适合公共卫生政策制定者参考
乳腺癌筛查对早期发现和有效管理至关重要，直接影响患者预后与生存率。本研究评估了2018年和2020年美国全国范围内的乳腺癌筛查率，并探究健康社会决定因素的影响。数据来自行为风险因素监测系统（BRFSS），覆盖72,337个普查区的乳腺钼靶检查情况。构建包含13个社会决定因素变量的大规模数据集，采用Getis-Ord Gi统计方法进行空间分析，识别高低筛查率聚集区。通过随机森林模型评估各因素影响，对比线性回归与支持向量机模型，使用R²与均方根误差（RMSE）评估性能。结果表明，东部和北部地区筛查率较高，而中西部较低。随机森林表现最优，R²=64.53，RMSE=2.06。SHAP值分析显示，黑人人口比例、10英里内钼靶设施数量及拥有学士学位人口比例为最显著正向影响因素。
原文摘要 · Abstract (English)
Breast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and survival rates. This study aims to assess breast cancer screening rates nationwide in the United States and investigate the impact of social determinants of health on these screening rates. Data on mammography screening at the census tract level for 2018 and 2020 were collected from the Behavioral Risk Factor Surveillance System. We developed a large dataset of social determinants of health, comprising 13 variables for 72337 census tracts. Spatial analysis employing Getis-Ord Gi statistics was used to identify clusters of high and low breast cancer screening rates. To evaluate the influence of these social determinants, we implemented a random forest model, with the aim of comparing its performance to linear regression and support vector machine models. The models were evaluated using R2 and root mean squared error metrics. Shapley Additive Explanations values were subsequently used to assess the significance of variables and direction of their influence. Geospatial analysis revealed elevated screening rates in the eastern and northern United States, while central and midwestern regions exhibited lower rates. The random forest model demonstrated superior performance, with an R2=64.53 and root mean squared error of 2.06 compared to linear regression and support vector machine models. Shapley Additive Explanations values indicated that the percentage of the Black population, the number of mammography facilities within a 10-mile radius, and the percentage of the population with at least a bachelor's degree were the most influential variables, all positively associated with mammography screening rates.
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