Interpretable Machine Learning–Derived Nomogram Model to Predict Population Health Literacy: Cross-Sectional Development and Validation Study

Background: Health literacy (HL) is a major factor associated with population health. However, no visualized predictive model has been developed to identify populations with low HL in a cost-effective manner. Objective: This study aims to identify key determinants of HL and develop a predictive nomogram model for population-level HL screening. Methods: This study analyzed cross-sectional data from 56,340 residents (aged 15-69 y) in Zhejiang Province (2023) And used the Chinese Health Literacy Scale to measure HL. We used 3 complementary analytical approaches: logistic regression (LR) analysis to identify HL-associated factors, machine learning models (gradient boosting decision tree [GBDT], extreme gradient boosting [XGBoost], LR, and random forest [RF]) with Shapley additive explanations (SHAP) to quantify predictor importance, and nomogram development using 2023 data with external validation on 2024 data. In the nomogram, model 1 used all the predictive variables, while model 2 used the 5 core predictive variables determined through SHAP analysis. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis. Results: The nomogram models designed for predicting HL demonstrated robust stability, with good results in external validation. The area under the curve (AUC) values for both the test and validation sets of the machine learning models exceeded 0.75. At the 0.5 threshold, the sensitivities of the GBDT, XGBoost, LR, and RF test sets and validation sets were all >60%. The specificity of the test sets and validation sets of all 4 machine learning models was >75%. The positive predictive value (PPV) and negative predictive value (NPV) were both >60%. The AUC value of nomogram model 1 developed using the data from 2023 was 0.821, and the AUC value of the externally validated nomogram model 1 was 0.814. Similarly, nomogram model 2 had AUC values of 0.819 in the internal validation and 0.812 in the external validation. The PPV and NPV of both the internal validation model 1 and model 2 were all >60%. The integration of GBDT, XGBoost, LR, and RF models, along with the SHAP method, highlighted education level, age, annual household income, smoking, and occupation as the 5 most critical determinants. Conclusions: By using the GBDT, XGBoost, LR, RF, and nomogram models, a predictive model with substantial efficacy was effectively developed and validated for forecasting the HL levels of the population. This comprehensive model not only exhibits a commendable predictive capacity but also identifies the pivotal factors influencing HL. Such insights are crucial for shaping health policies and fostering healthier behaviors across the population.