From Nutritional Epidemiology to Clinical Prediction: Interpretable Machine Learning Reveals Subtle Associations Between Dietary Antioxidant Profiles and Non-Melanoma Skin Cancer in Older Adults.
- 1Department of Health Industry Sookmyung Women's University Seoul Republic of Korea.
Abstract
The growing burden of non-melanoma skin cancer (NMSC) among older adults underscores the need for early risk prediction based on modifiable dietary factors. In this cross-sectional study using data from the 2007-2010 and 2017-2018 cycles of the National Health and Nutrition Examination Survey (NHANES), we evaluated dietary antioxidant profiles and developed an interpretable machine learning model for NMSC risk prediction. Baseline feature analysis revealed that participants with NMSC generally had higher socioeconomic status and higher oxidative balance scores (OBS), with significant differences also observed in age and race. Through feature importance ranking using the Random Forest (RF) algorithm, caffeine and luteolin were identified as the most predictive dietary variables. After comparing 12 machine learning algorithms, linear discriminant analysis (LDA) was selected as the final screening-oriented prediction model (AUC = 0.742). Although its overall accuracy was lower than that of several ensemble models, LDA demonstrated higher sensitivity (0.730) and favorable imbalance-aware performance, thereby mitigating the "accuracy paradox" and supporting its use for preliminary screening to identify participants with higher predicted NMSC risk. SHapley Additive exPlanations (SHAP) global explanations further confirmed that, after controlling for age as a primary risk factor, niacin and quercetin act as protective factors in the NMSC risk prediction model. In summary, this study developed an interpretable and robust machine learning model for NMSC risk prediction in older adults and provides hypothesis-generating evidence regarding the potential role of dietary antioxidant profiles in prevention-oriented strategies.
Related
- Soy Consumption and the Risk of Developing Bladder Cancer: A Pooled Analysis of Cohort Studies from the International Pooled Bladder Cancer Epidemiology and Nutritional Determinants (BLEND) Study
- Preoperative risk stratification for postoperative inflammatory–nutritional deterioration after colorectal cancer surgery: an interpretable machine learning study
- Innovative Cookies Based on Watermelon Rind and Okra: Nutritional Content, Total Phenolic Content, and Antioxidant Activity
- Associations Between Serum Osteocalcin and Metabolic, Nutritional, and Hormonal Status in Male Patients with Chronic Kidney Disease: A Cross-Sectional Study
- Dual-Pigmented Rice: A Dual-Pigmented Whole Grain with Enhanced Nutritional and Antioxidant Properties.
BibTeX
@article{yu2026from,
title = {{From Nutritional Epidemiology to Clinical Prediction: Interpretable Machine Learning Reveals Subtle Associations Between Dietary Antioxidant Profiles and Non-Melanoma Skin Cancer in Older Adults.}},
author = {Bo Han Yu and Ji Young Kang and Kyung Eun Kim},
journal = {Food science \& nutrition},
year = {2026},
month = oct,
doi = {10.1002/fsn3.72458},
url = {https://europepmc.org/article/MED/42903526},
note = {CC BY},
}
RIS
TY - JOUR
TI - From Nutritional Epidemiology to Clinical Prediction: Interpretable Machine Learning Reveals Subtle Associations Between Dietary Antioxidant Profiles and Non-Melanoma Skin Cancer in Older Adults.
AU - Bo Han Yu
AU - Ji Young Kang
AU - Kyung Eun Kim
PY - 2026
DA - 2026/10/20
JO - Food science & nutrition
DO - 10.1002/fsn3.72458
UR - https://europepmc.org/article/MED/42903526
AB - The growing burden of non-melanoma skin cancer (NMSC) among older adults underscores the need for early risk prediction based on modifiable dietary factors. In this cross-sectional study using data from the 2007-2010 and 2017-2018 cycles of the National Health and Nutrition Examination Survey (NHANES), we evaluated dietary antioxidant profiles and developed an interpretable machine learning model for NMSC risk prediction. Baseline feature analysis revealed that participants with NMSC generally had higher socioeconomic status and higher oxidative balance scores (OBS), with significant differences also observed in age and race. Through feature importance ranking using the Random Forest (RF) algorithm, caffeine and luteolin were identified as the most predictive dietary variables. After comparing 12 machine learning algorithms, linear discriminant analysis (LDA) was selected as the final screening-oriented prediction model (AUC = 0.742). Although its overall accuracy was lower than that of several ensemble models, LDA demonstrated higher sensitivity (0.730) and favorable imbalance-aware performance, thereby mitigating the "accuracy paradox" and supporting its use for preliminary screening to identify participants with higher predicted NMSC risk. SHapley Additive exPlanations (SHAP) global explanations further confirmed that, after controlling for age as a primary risk factor, niacin and quercetin act as protective factors in the NMSC risk prediction model. In summary, this study developed an interpretable and robust machine learning model for NMSC risk prediction in older adults and provides hypothesis-generating evidence regarding the potential role of dietary antioxidant profiles in prevention-oriented strategies.
SN - 2048-7177
ER -