ArticleScientific reports2021
Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 42 citations in OpenAlex.
- Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.BMC medical informatics and decision making · 2026Article
- Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States.JCO clinical cancer informatics · 2025Article
- Population Digital Health: Continuous Health Monitoring and Profiling at Scale.Online journal of public health informatics · 2024Article
- Article
- Insights from Explainable Artificial Intelligence of Pollution and Socioeconomic Influences for Respiratory Cancer Mortality in Italy.Journal of personalized medicine · 2024Article
- Predicting blood lead in Uruguayan children: Individual- vs neighborhood-level ensemble learners.PLOS global public health · 2024Article
- Predicting Low-Level Childhood Lead Exposure in Metro Atlanta Using Ensemble Machine Learning of High-Resolution Raster Cells.International journal of environmental research and public health · 2023Article
- Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review.Translational cancer research · 2022Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to mortality rate requires explainability. We applied explainable artificial intelligence (XAI) on a stack-ensemble machine learning model framework to explore and visualize the spatial distribution of the contributions of known risk factors to lung and bronchus cancer (LBC) mortality rates in the conterminous United States. We used five base-learners-generalized linear model (GLM), random forest (RF), Gradient boosting machine (GBM), extreme Gradient boosting machine (XGBoost), and Deep Neural Network (DNN) for developing stack-ensemble models. Then we applied several model-agnostic approaches to interpret and visualize the stack ensemble model's output in global and local scales (at the county level). The stack ensemble generally performs better than all the base learners and three spatial regression models. A permutation-based feature importance technique ranked smoking prevalence as the most important predictor, followed by poverty and elevation. However, the impact of these risk factors on LBC mortality rates varies spatially. This is the first study to use ensemble machine learning with explainable algorithms to explore and visualize the spatial heterogeneity of the relationships between LBC mortality and risk factors in the contiguous USA.
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