Journal of the Royal Society of New Zealand
Study on the Prediction of Diabetes-Cancer Comorbidity Based on Dietary Factors Explained by Seven Machine Learning Algorithms and SHapley Additive exPlanations
By Yang Y and Fang B.
Abstract
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.