从废弃物中学习：加纳健康风险预测的机器学习与基于计算机视觉的分类
Learning from waste: Machine Learning for health risk prediction and computer vision-based sorting in Ghana
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中文总结 AI 辅助
本研究针对加纳废物处置与疾病的关联问题，用随机森林模型预测疾病类别、MobileNetV2模型实现废物自动分类，为该关联提供定量证据，同时强调制度支持对公共卫生改善的必要性。
中文摘要 AI 辅助
固体废物的不当处置仍是全球范围内包括加纳在内的重大公共卫生与环境问题。恶劣的卫生条件与不当的废物管理实践每年造成巨额经济损失与本可避免的死亡。2022年，在加纳库马西阿通苏开展的一项实地研究报告称，社区感知到家庭废物处置与疾病模式存在关联，但仅通过描述性分析得出，未进行定量验证。本研究采用两种数据驱动方法拓展该调查：其一，开发了随机森林（Random Forest）分类器，利用废物处置实践与人口统计调查数据预测疾病类别；在一组报告患病的留存受访者（N=69）中，该模型的宏F1值为0.63，其中处置方法成为疾病类型最重要的实质性预测因子。其二，开发了MobileNetV2图像分类模型，通过视觉识别实现废物自动分类，在测试集（N=415）上达到88.2%的准确率与0.87的宏F1值；该基于视觉的方法提供了一种经济实惠、以相机驱动的替代复杂多传感器系统的方案，非常适合资源受限的环境。综合来看，研究结果为此前仅定性记录的社区健康关联提供了定量证据，证明了在低资源环境中自动废物分类的潜力。重要的是，结果表明仅技术性能本身无法保证公共卫生改善，有效的制度支持与实施同样必要。
英文摘要
The inappropriate disposal of solid waste remains a significant public health and environmental concern worldwide, including in Ghana. Poor sanitation and improper waste management practices contribute to substantial economic costs and avoidable deaths annually. In 2022, a field study in Atonsu, Kumasi, Ghana, reported a community-perceived relationship between household waste disposal and illness patterns, but only through descriptive analysis without quantitative validation. This study extends that investigation using two data-driven approaches. First, a Random Forest classifier was developed to predict illness categories using waste disposal practices and demographic survey data. On a held-out group of respondents who reported illness (N=69), the model obtained a macro F1 score of 0.63, with disposal method emerging as the most important substantive predictor of illness type. Second, a MobileNetV2 image classification model enabled automated waste sorting via visual recognition, achieving 88.2% accuracy and a macro F1 score of 0.87 on the test set (N=415). The vision-based approach offers an affordable, camera-driven alternative to complex multi-sensor systems, making it highly suitable for resource-constrained settings. Taken together, the findings provide quantitative evidence for a community health relationship previously documented only qualitatively. They demonstrate the potential for automated waste-sorting in low-resource environments. Importantly, the results illustrate that technological performance alone does not guarantee public health improvements; effective institutional support and implementation are equally necessary.
发表机构
- Kwame Nkrumah University of Science and Technology（夸梅·恩克鲁玛科技大学）
- University of Nottingham（诺丁汉大学）
机构由 AI 辅助整理，请以论文原文为准。
