Optimizing Public Health Management with Predictive Analytics: Leveraging the Power of Random Forest"

Hongman Wang¹Yifan Song^2*Hua Bi³

• ¹Southeast University, Nanjing, Jiangsu Province, China
• ²Macao Polytechnic University, Macau, Macao, SAR China
• ³General Hospital of the Central Theater of the People's Liberation Army, Wuhan, Hebei Province, China

Abstract：Community health outcomes significantly impact older populations' well-being and quality of life. Traditional analytical methods often struggle to accurately predict health risks at the community level due to their inability to capture complex, non-linear relationships among various health determinants. This study employs a Random Forest Algorithm (RFA) to address this limitation and enhance the predictive modelling of community health outcomes. By leveraging ensemble learning techniques and multi-factor analysis, the study aims to identify and quantify the relative contribution of key health indicators to risk assessment. The study begins with comprehensive data collection from diverse health sources, followed by a systematic preprocessing stage, which includes resolving missing values, normalizing variables, and encoding categorical features. Using bootstrap sampling, multiple decision trees were trained on random subsets of health data, ensuring variability in model learning. The trees grow to full depth and aggregate their predictions to enhance accuracy. Out-of-bag (OOB) error estimation was applied to refine the model and provide unbiased performance assessments, ensuring robust generalization to unseen data. The proposed model effectively analyses key health indicators, ranking feature importance to determine the most influential predictors of health risk. Results indicate that RFA achieves an accuracy rate of 92%, outperforming conventional prediction methods in precision and recall. These findings underscore the efficacy of Random Forest in identifying critical health risk factors, paving the way for more targeted, data-driven public health management strategies and interventions tailored to older adults.