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Cited by 284Artificial Intelligence in HealthcareNutritional Studies and DietAntioxidant Activity and Oxidative Stress

The DOI matches its Crossref record; 61 of the 64 references it cites were found in a registry or an index.

Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants

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Qi, Xiangjun, Wang, Shujing, Fang, Caishan, Jia, Jie, Lin, Lizhu, Yuan, Tianhui·2025·Redox Biology
DOI 10.1016/j.redox.2024.103470PMC PMC11729017
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Abstract

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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Crossref · 10.1016/j.redox.2024.103470

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Fig. 1 Participants screening flow chart.
Fig. 1 Participants screening flow chart.CC BY · PubMed Central
Fig. 2 Correlation among remaining dietary antioxidant features after removing highly correlated ones.
Fig. 2 Correlation among remaining dietary antioxidant features after removing highly correlated ones.CC BY · PubMed Central
Fig. 3 Receiver operating characteristic curves for the 5 machine learning models in predicting cardiovascular disease and cancer comorbidity. (A). Receiver operating characteristic curves. (B). Areas under the receiver operating characteristic curves.
Fig. 3 Receiver operating characteristic curves for the 5 machine learning models in predicting cardiovascular disease and cancer comorbidity. (A). Receiver operating characteristic curves. (B). Areas under the receiver operating characteristic curves.CC BY · PubMed Central
Fig. 4 Precision-recall curves for the 5 machine learning models in predicting cardiovascular disease and cancer comorbidity. (A) Precision-recall curves. (B) Areas under the precision-recall curves.
Fig. 4 Precision-recall curves for the 5 machine learning models in predicting cardiovascular disease and cancer comorbidity. (A) Precision-recall curves. (B) Areas under the precision-recall curves.CC BY · PubMed Central
Fig. 5 SHAP values of dietary antioxidants for LightGBM model. (A). SHAP summary plot. (B) SHAP waterfall plot. (C). SHAP force plot.
Fig. 5 SHAP values of dietary antioxidants for LightGBM model. (A). SHAP summary plot. (B) SHAP waterfall plot. (C). SHAP force plot.CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Lin, 2020, Naringin induces endoplasmic reticulum stress-mediated apoptosis, inhibits β-catenin pathway and arrests cell cycle in cervical cancer cells, Acta Biochim. Pol., 67, 181

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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
ORCID
Venue
Redox Biology
Publisher
Elsevier BV
Published
2025-02
Volume
79
Pages
103470
ISSN
2213-2317 (ISSN-L)
DOI
10.1016/j.redox.2024.103470
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
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

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