ArticleInternational journal of environmental research and public health2024
Random Forest and Feature Importance Measures for Discriminating the Most Influential Environmental Factors in Predicting Cardiovascular and Respiratory Diseases.
Article in International journal of environmental research and public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Predicting Insecticide-Treated Net Use Among Under-Five Children in Tanzania Using Machine Learning: Evidence From the 2022 Tanzania DHS.Health science reports · 2026Article
- Machine-learning algorithms to identify key practices contributing to variation in biosecurity levels on Japanese commercial swine farms.The Journal of veterinary medical science · 2026Article
- Estimating the odds ratio from the output scores of machine learning models: possibilities and limitations.Scientific reports · 2026Article
- Sustainable design of organic solar cells utilized machine and deep learning.Scientific reports · 2026Article
- Machine learning approaches for data-driven hydrocarbon bioaugmentation and phytoremediation: the role of multi-omics insights.Frontiers in microbiology · 2026Review
- Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species.Royal Society open science · 2025Article
- Environmental Pollutants as Emerging Concerns for Cardiac Diseases: A Review on Their Impacts on Cardiac Health.Biomedicines · 2025Review
- Prediction of respiratory diseases based on random forest model.Frontiers in public health · 2025Article
- Machine Learning-Based Random Forest to Predict 3-Year Survival after Endovascular Aneurysm Repair.Annals of thoracic and cardiovascular surgery : official journal of the Association of Thoracic and Cardiovascular Surgeons of Asia · 2025Article
- A hybrid ensemble approach for diabetes prediction using consensus-based feature selection.Digital healthArticle
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4 authors.
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Abstract
backgroundSeveral studies suggest that environmental and climatic factors are linked to the risk of mortality due to cardiovascular and respiratory diseases; however, it is still unclear which are the most influential ones. This study sheds light on the potentiality of a data-driven statistical approach by providing a case study analysis.
methodsDaily admissions to the emergency room for cardiovascular and respiratory diseases are jointly analyzed with daily environmental and climatic parameter values (temperature, atmospheric pressure, relative humidity, carbon monoxide, ozone, particulate matter, and nitrogen dioxide). The Random Forest (RF) model and feature importance measure (FMI) techniques (permutation feature importance (PFI), Shapley Additive exPlanations (SHAP) feature importance, and the derivative-based importance measure (κALE)) are applied for discriminating the role of each environmental and climatic parameter. Data are pre-processed to remove trend and seasonal behavior using the Seasonal Trend Decomposition (STL) method and preliminary analyzed to avoid redundancy of information.
resultsThe RF performance is encouraging, being able to predict cardiovascular and respiratory disease admissions with a mean absolute relative error of 0.04 and 0.05 cases per day, respectively. Feature importance measures discriminate parameter behaviors providing importance rankings. Indeed, only three parameters (temperature, atmospheric pressure, and carbon monoxide) were responsible for most of the total prediction accuracy.
conclusionsData-driven and statistical tools, like the feature importance measure, are promising for discriminating the role of environmental and climatic factors in predicting the risk related to cardiovascular and respiratory diseases. Our results reveal the potential of employing these tools in public health policy applications for the development of early warning systems that address health risks associated with climate change, and improving disease prevention strategies.
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