Abstract
This study investigates the relationship between dietary patterns and psoriasis severity using advanced machine learning (ML) techniques. The dataset, comprising 37 features including demographic, clinical and dietary features from 142 Thai psoriasis patients, exhibits moderately high dimensionality typical of clinical studies. To address limitations posed by the small sample size, a hybrid resampling strategy integrating bootstrapping with K-fold Cross-Validation (CV) was implemented. Using Random Forest (RF) and eXtreme Gradient Boosting (XGB), a total of 60 classification models were evaluated by varying train/test splits and applying multiple feature selection methods, including Least Absolute Shrinkage and Selection Operator (LASSO), Mean Decrease Accuracy (MDA), and Mean Decrease Impurity (MDI). Although bootstrapping alone sometimes resulted in overfitting, its combination with K-fold CV improved generalizability. In optimal configurations, both RF and XGB achieved sensitivity, specificity, and F1-scores exceeding 90%, alongside area under the curve (AUC) values above 95%. SHapley Additive exPlanations (SHAP) analysis revealed key dietary factors associated with increased psoriasis severity, including high-sodium foods, processed meats, alcohol, red meats, fermented products, and dark-colored vegetables. Clinically, prioritizing weight management is essential, as Body Mass Index (BMI) arose as the strongest feature of psoriasis severity. Dietary triggers identified in this study should inform comprehensive care plans. Popular Thai cuisines, especially Tom Yum Kung emerged as a potentially suitable option, while Som Tum, Pad Thai, Moo Kratha, and Khao Niao Mamuang were identified as potential triggers when consumed excessively. These findings highlight the importance of dietary moderation and personalized guidance, supporting health literacy, patient management, and smart healthcare innovations in Thailand.