Abstract: The environment exerts a prolonged influence on human health and the progression of diseases.Therefore,investigating the impact of climatic conditions on individual health and improving the accuracy of disease prediction models is of great significance for disease prevention and control.Currently,research integrating meteorological data with disease studies predominantly focuses on qualitative analysis or macro-level predictions at the population level,while quantitative modeling addressing indivi-dual differential responses remains relatively scarce.To address this issue,this paper proposes a chronic disease prediction method based on interaction models that integrates meteorological data with individual health data.The method utilizes data from the CLHLS-HF(Chinese Longitudinal Healthy Longevity and Happy Family Study) and theCHARLS(China Health and Retirement Longitudinal Study),employing Moran's I statistic to identify geographically distributed diseases with spatial correlation.Interaction models are used to integrate individual health data and meteorological data,constructing interaction features to enhance the performance of disease prediction models.Four machine learning models—logistic regression,Naive Bayes,XGBoost,and multilayer perceptron(MLP)—are applied to both datasets for disease prediction to evaluate the impact of incorporating meteorological features.Experimental results show that in the CLHLS-HF dataset,the Naive Bayes model improves specificity by 10.2% for dyslipidemia prediction,while the XGBoost model improves accuracy and sensitivity by 5.6%.In the CHARLS dataset,the multilayer perceptron model improves the AUC for heart disease prediction by 6.6%,and the XGBoost model improves sensitivity by 6.2%.These findings demonstrate that incorporating meteorological data and interaction features consistently enhances the performance of disease prediction models.

Key words: Meteorological factors, Disease prediction, Interaction models, Machine learning