Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States
The RF model demonstrated superior predictive performance for LC mortality rates, emphasizing the critical roles of smoking prevalence, housing values, and the percentage of Hispanic ethnic population. These findings offer valuable actionable insights for designing targeted interventions, promoting screening, and addressing health disparities in regions most affected by LC in the United States.
Authors
- Brianna M White (ORCID: https://orcid.org/0000-0001-7576-5874)
- Arash Shaban‐Nejad (ORCID: https://orcid.org/0000-0003-2047-4759)
- Benyamin Hoseini (ORCID: https://orcid.org/0000-0002-0355-6181)
- Soheil Hashtarkhani (ORCID: https://orcid.org/0000-0001-7750-6294)
- David L. Schwartz (ORCID: https://orcid.org/0000-0002-7235-5586)
Institutions
- University of Tennessee Health Science Center (US)
- Mashhad University of Medical Sciences (IR)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-11-01
- DOI
- https://doi.org/10.1200/cci-24-00310
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00