Study on the Prediction of Diabetes-Cancer Comorbidity Based on Dietary Factors Explained by Seven Machine Learning Algorithms and SHapley Additive exPlanations.

Background

The bidirectional link between diabetes and cancer is well established, but the role of dietary factors in predicting their comorbidity remains unclear. This study aims to develop a machine learning model to integrate dietary components and to identify key predictors using SHAP analysis.

Methods

Data were derived from the NHANES 2009-2012 and 2015-2018 cycles, including 20,307 participants (682 with diabetes-cancer comorbidity). After eliminating collinearity and standardizing, 41 dietary features were retained. Seven algorithms-Naive Bayes, support vector machine with radial basis kernel and class weights (SVM-RW), random forest (RF), weighted k-nearest neighbors (weighted k-NN), AdaBoost, LogitBoost, and cancerclass-were trained and evaluated by benchmark tests using AUC-ROC and AUC-PR. The best-performing model was interpreted with SHAP values.

Results

AdaBoost achieved the highest discrimination, with AUC-ROC of 0.898 and AUC-PR of 0.851. SHAP analysis revealed that vitamins (D, K, A, C, thiamin, total folate, niacin, and β-carotene), minerals (calcium, iron, sodium, and copper), and carbohydrates/dietary fiber were the most influential features. Among these, carbohydrates and calcium exerted the dominant negative contribution to comorbidity prediction, indicating that higher intake was associated with a lower predicted probability of diabetes-cancer comorbidity by the model.

Conclusions

The AdaBoost model exhibited the best performance for predicting diabetes-cancer comorbidity. Dietary components played an important role in disease prediction, among which carbohydrates and calcium served as core negative predictive factors. The identified associations provide clues and evidence for further research on dietary interventions for high-risk populations.