SUPERVISED LEARNING-BASED DETECTION OF ASTHMA-AIR POLLUTION ASSOCIATIONS IN URBAN ENVIRONMENTS
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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Zenodo
2024
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| _version_ | 1866901348605231104 |
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| author | JOAE |
| author_facet | JOAE |
| contents | <p>Asthma is a prevalent chronic respiratory disease affecting millions worldwide, with air pollution recognized as a significant environmental trigger that exacerbates symptoms and increases hospitalizations. Understanding the intricate relationship between asthma and air pollution is essential for developing effective public health strategies. Traditional epidemiological studies rely on extensive data collection and statistical analysis to identify correlations, but these methods often face challenges such as time constraints, data limitations, and an inability to capture real-time associations. With the rise of machine learning applications in pollution monitoring, supervised learning algorithms offer a powerful approach to uncover complex patterns and associations between asthma prevalence and air pollution levels in urban regions. This research aims to develop an accurate and reliable predictive model that leverages machine learning techniques to analyze environmental and health data. The goal is to enhance proactive decision-making by enabling healthcare providers to allocate resources efficiently and empowering policymakers to implement targeted interventions. By integrating advanced supervised learning methods, this study aspires to minimize the impact of air pollution on asthma patients and contribute to healthier urban environments through data-driven public health policies</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15799266 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | SUPERVISED LEARNING-BASED DETECTION OF ASTHMA-AIR POLLUTION ASSOCIATIONS IN URBAN ENVIRONMENTS JOAE Asthma, Air pollution, Machine learning, Supervised learning, Predictive model <p>Asthma is a prevalent chronic respiratory disease affecting millions worldwide, with air pollution recognized as a significant environmental trigger that exacerbates symptoms and increases hospitalizations. Understanding the intricate relationship between asthma and air pollution is essential for developing effective public health strategies. Traditional epidemiological studies rely on extensive data collection and statistical analysis to identify correlations, but these methods often face challenges such as time constraints, data limitations, and an inability to capture real-time associations. With the rise of machine learning applications in pollution monitoring, supervised learning algorithms offer a powerful approach to uncover complex patterns and associations between asthma prevalence and air pollution levels in urban regions. This research aims to develop an accurate and reliable predictive model that leverages machine learning techniques to analyze environmental and health data. The goal is to enhance proactive decision-making by enabling healthcare providers to allocate resources efficiently and empowering policymakers to implement targeted interventions. By integrating advanced supervised learning methods, this study aspires to minimize the impact of air pollution on asthma patients and contribute to healthier urban environments through data-driven public health policies</p> |
| title | SUPERVISED LEARNING-BASED DETECTION OF ASTHMA-AIR POLLUTION ASSOCIATIONS IN URBAN ENVIRONMENTS |
| topic | Asthma, Air pollution, Machine learning, Supervised learning, Predictive model |
| url | https://doi.org/10.5281/zenodo.15799266 |