Optimizing public health management with predictive analytics: leveraging the power of random forest
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 deter...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2025.1574683/full |
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| author | Hongman Wang Yifan Song Yifan Song Hua Bi |
| author_facet | Hongman Wang Yifan Song Yifan Song Hua Bi |
| author_sort | Hongman Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-df0c07b4802c4bf4bf61ece19630aac1 |
| institution | Kabale University |
| issn | 2624-909X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj-art-df0c07b4802c4bf4bf61ece19630aac12025-08-20T03:28:55ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2025-07-01810.3389/fdata.2025.15746831574683Optimizing public health management with predictive analytics: leveraging the power of random forestHongman Wang0Yifan Song1Yifan Song2Hua Bi3School of Humanities, Southeast University, Nanjing, ChinaSchool of Humanities, Southeast University, Nanjing, ChinaFaculty of Humanities and Social Sciences, Macao Polytechnic University, Macau, Macao SAR, ChinaGeneral Hospital of the Central Theater of the People's Liberation Army, Wuhan, ChinaCommunity 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.https://www.frontiersin.org/articles/10.3389/fdata.2025.1574683/fullpredictive analyticsrandom forestpublic healthhealth risk factorsmachine learningcommunity-based health management |
| spellingShingle | Hongman Wang Yifan Song Yifan Song Hua Bi Optimizing public health management with predictive analytics: leveraging the power of random forest Frontiers in Big Data predictive analytics random forest public health health risk factors machine learning community-based health management |
| title | Optimizing public health management with predictive analytics: leveraging the power of random forest |
| title_full | Optimizing public health management with predictive analytics: leveraging the power of random forest |
| title_fullStr | Optimizing public health management with predictive analytics: leveraging the power of random forest |
| title_full_unstemmed | Optimizing public health management with predictive analytics: leveraging the power of random forest |
| title_short | Optimizing public health management with predictive analytics: leveraging the power of random forest |
| title_sort | optimizing public health management with predictive analytics leveraging the power of random forest |
| topic | predictive analytics random forest public health health risk factors machine learning community-based health management |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2025.1574683/full |
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