[Objective] This paper makes disease prediction models balance computational efficiency and prediction accuracy by selecting key disease risk variables, aiming to help public health departments achieve efficient disease early warning. [Methods] We used ensemble learning-based Random Forest and XGBoost models to learn high-dimensional disease risk variable data for disease prediction. The models autonomously select subsets of variables that contribute to their prediction. To ensure that the selected subset has high prediction accuracy, we analyze the ensemble strategy of Random Forest and XGBoost. By adjusting hyperparameters and cross-validating, we improved the out-of-bag error rate of the Random Forest model iteratively and converged the loss curve of the XGBoost model on different sub-training sets. Finally, we proposed unique optimization solutions for each model to enhance their disease prediction performance. [Results] We examined the optimized models with the dataset of geriatric depression and anxiety. They exhibited excellent and comparable disease prediction performance, achieving prediction accuracies of 88.6% and 89.7%, as well as AUC values of 0.936 and 0.940, respectively. However, the XGBoost model had a simpler and more efficient structure with the optimized feature selection. It selected only 17 key variables out of 54 geriatric depression and anxiety risk variables, achieving a prediction accuracy of 85.8% and an AUC of 0.917. [Limitations] We did not utilize the latest geriatric cohort data for experimentation. More research is needed to test the adaptability of models in complex and heterogenous data environments. [Conclusions] The feature selection effect of the optimized XGBoost model is superior in improving the efficiency of disease early warning and providing decision support for public health management.