JMIR medical informatics
Interpretable Machine Learning-Derived Nomogram Model to Predict Population Health Literacy: Cross-Sectional Development and Validation Study
By Zhang X, Jin H, Xie Z and 10 others.
Abstract
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.