Construction of risk prediction models for psoriasis based on 45 dietary nutrients using machine learning and SHAP analysis.

Background and objectivesTo develop and validate a machine learning model to predict the risk of psoriasis based on 45 dietary nutrients.Methods and study design12,749 participants from the National Health and Nutrition Examination Survey from 2009-2014 were included and their demographic, lifestyle, health status, and dietary nutrient-related information is collected. Imbalanced data were processed using the syn-thetic minority oversampling technique (SMOTE). After removing the covariate features, important features were further screened using the Boruta algorithm and six machine learning models were constructed including Random Forest (RF), Light Gradient Boosting Machine (Light GBM), Kernel K-Nearest Neighbor (K-KNN), L Naive Bayes, Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost). The performance of the models was evaluated using benchmarking and the area under the ROC curve (AUC) was the main evaluation metric to choose the optimal machine learning model. Shapley additive explanation (SHAP) values were computed to evaluate each feature's prediction role in the mode.ResultsThe Boruta algorithm screened 10 baseline features and 23 dietary nutrient features, and six machine learning models were developed based on them. Compared with other machine learning models, XGBoost demonstrated superior prediction ability. SHAP analysis showed that theobromine, lycopene, caffeine, vitamin D, dietary fiber and vitamin E were the key features that influenced the prediction results.ConclusionsThe machine learning (ML) algorithm constructed a prediction model for psoriasis by incorporating baseline features and dietary nutrient features. The SHAP values indicate the dominant role of dietary nutrients in the model.