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Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants
Open accessAbstract
OBJECTIVE: To develop and validate a machine learning model incorporating dietary antioxidants to predict cardiovascular disease (CVD)-cancer comorbidity and to elucidate the role of antioxidants in disease prediction. METHODS: Data were sourced from the National Health and Nutrition Examination Survey. Antioxidants, including vitamins, minerals, and polyphenols, were selected as key features. Additionally, demographic, lifestyle, and health condition features were incorporated to improve model accuracy. Feature preprocessing included removing collinear features, addressing class imbalance, and normalizing data. Models constructed within the mlr3 framework included recursive partitioning and regression trees, random forest, kernel k-nearest neighbors, naïve bayes, and light gradient boosting machine (LightGBM). Benchmarking provided a systematic approach to evaluating and comparing model performance. SHapley Additive exPlanation (SHAP) values were calculated to determine the prediction role of each feature in the model with the highest predictive performance. RESULTS: This analysis included 10,064 participants, with 353 identified as having comorbid CVD and cancer. After excluding collinear features, the machine learning model retained 29 dietary antioxidant features and 9 baseline features. LightGBM achieved the highest predictive accuracy at 87.9 %, a classification error rate of 12.1 %, and the top area under the receiver operating characteristic curve (0.951) and the precision-recall curve (0.930). LightGBM also demonstrated balanced sensitivity and specificity, both close to 88 %. SHAP analysis indicated that naringenin, magnesium, theaflavin, kaempferol, hesperetin, selenium, malvidin, and vitamin C were the most influential contributors. CONCLUSION: LightGBM exhibited the best performance for predicting CVD-cancer comorbidity. SHAP values highlighted the importance of antioxidants, with naringenin and magnesium emerging as primary factors in this model.
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“SHAP values provide a consistent and locally accurate assessment of the contribution of each feature to the model’s predictions, enabling a more transparent understanding of the relationships between input variables and outcomes [37, 38].”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as [37]
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“One key advantage of LGB is its ability to handle mixed-type features, this helps preserve the true distribution of these variables within the model.”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as [37]
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“Additionally, LGB uses a histogram-based decision tree approach, discretizing continuous features during the data-splitting process, and thereby allowing for effective management of sparse data.”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as [37]
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“Furthermore, this model is particularly well equipped to address the challenges posed by imbalanced datasets.”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as [37]
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Citations
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Metadata
- Authors
- Qi, X., Wang, S., Fang, C., Jia, J., Lin, L., & Yuan, T.
- Affiliations
- Guangzhou University of Chinese Medicine (CN)
- National University of Singapore (SG)
- The University of Sydney (AU)
- First Affiliated Hospital of Guangzhou University of Chinese Medicine (CN)
- Corresponding author
- Lin, Lizhu
- Yuan, Tianhui
- Venue
- Redox Biology
- Publisher
- Elsevier BV
- Published
- 2025-02
- Volume
- 79
- Pages
- 103470
- ISSN
- 2213-2317 (ISSN-L)
- PMC
- PMC11729017
- OpenAlex
- W4405439214
- Cited by
- 271
- References
- 64
- Citations by year
- 2026: 170 · 2025: 101
- FWCI
- 82.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Redox Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China
- Guangzhou Municipal Science and Technology Bureau (SL2022B01J10007)
- National Key Research and Development Program of China
- Guangdong Special Support Plan
- Basic and Applied Basic Research Foundation of Guangdong Province
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
- Health Sciences › Medicine › Biochemistry (medical) › Antioxidant Activity and Oxidative Stress
- Keywords
- cardiovascular disease, cancer, comorbidity prediction, dietary antioxidants, polyphenols, LightGBM, SHAP values, naringenin, magnesium, vitamin C, selenium, theaflavins, kaempferol, hesperetin, malvidin, minerals
- MeSH terms
- Machine Learning, Adult, Aged, Antioxidants, Cardiovascular Diseases, Female, Humans, Male, Middle Aged, Neoplasms, Nutrition Surveys, ROC Curve, Comorbidity, Dietary Supplements
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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