Optimizing public health management with predictive analytics: leveraging the power of random forest.

Abstract

Community health outcomes significantly impact older populations' wellbeing 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 modeling of community health outcomes. By leveraging ensemble learning techniques and multi-factor analysis, this study aims to identify and quantify the relative contributions 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 the model learning. The trees grow to full depth and aggregate their predictions to enhance the accuracy. An 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 analyzes key health indicators, ranking the feature importance to determine the most influential predictors of health risks. Results indicate that RFA achieves an accuracy rate of 92%, outperforming conventional prediction methods in terms of precision and recall. These findings underscore the efficacy of Random Forest in identifying critical health risk factors, paving the way for targeted and data-driven public health management strategies and interventions tailored to older adults.