Machine learning and SHAP-based interpretation of dietary antioxidants' impact on comorbid MDD and suicidal ideation.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Random Forest model accuracy 83%

• Primary Outcome: Prediction of comorbid MDD and suicidal ideation

• Nnt Or Sensitivity: Precision 0.825, Recall 0.838, AUC 0.927

• Confidence Interval: Not reported

Clinical Application

Limited clinical feasibility due to complex antioxidant measurement requirements and need for machine learning infrastructure Dietary patterns differ from US population; would require validation in Australian cohorts. Potential integration with existing depression screening in primary care through RACGP guidelines, but significant implementation barriers exist Adults with potential depression and suicidal ideation risk, particularly those with dietary assessment data

Abstract

OBJECTIVE: To develop and validate a machine learning model for predicting major depressive disorder (MDD) with suicidal ideation (SI) by incorporating dietary antioxidants, and to elucidate the specific contribution of these antioxidants in the prediction. METHODS: Data were obtained from NHANES 2007-2010. Dietary antioxidants, including vitamins, minerals, and polyphenols, were the primary predictors. Demographic, lifestyle, and health-related variables were also included. Collinear variables were removed, class imbalance was corrected, and data were normalized prior to modeling. Twelve machine learning algorithms were compared using a systematic benchmarking protocol within the sklearn framework: Random Forest (RF), LightGBM, AdaBoost, XGBoost, Extra Trees, CatBoost, Gradient Boosting Decision Trees, Support Vector Machine, Decision Tree, Gaussian Naïve Bayes, Stochastic Gradient Descent, and K-Nearest Neighbors. SHapley Additive exPlanation (SHAP) values were calculated to interpret feature contributions within the best-performing model. RESULTS: A total of 9306 participants were included, of whom 247 with MDD comorbid with SI. After preprocessing, 31 dietary antioxidants and 9 demographic and health-related variables were retained for modeling. The RF classifier achieved the highest performance with an accuracy of 83%, precision of 0.825, recall of 0.838, area under the ROC curve of 0.927, and F1 score of 0.831. SHAP analysis identified vitamin C, kaempferol, myricetin, peonidin, luteolin, eriodictyol, hesperetin, pelargonidin, and zinc as the most influential predictors. CONCLUSION: The RF model exhibited superior predictive capability for comorbid MDD and SI. SHAP analysis highlighted the critical roles of dietary antioxidants, particularly vitamin C and kaempferol, in predicting these outcomes.

References

1. 1.Xu, Z., Wang, J., Chang, T., Li, Y., Jia, H., & Zhang, X. (2026). Machine learning and SHAP-based interpretation of dietary antioxidants' impact on comorbid MDD and suicidal ideation. Journal of Affective Disorders, 121252. https://doi.org/10.1016/j.jad.2026.121252