Learning from waste: Machine Learning for health risk prediction and computer vision-based sorting in Ghana

• URL: http://arxiv.org/abs/2608.25759v1
• Date: Wed, 26 Aug 2026 13:06:35 GMT
• Title: Learning from waste: Machine Learning for health risk prediction and computer vision-based sorting in Ghana
• Abstract summary: Poor sanitation and improper waste management practices contribute to substantial economic costs and avoidable deaths annually.<n>This study extends that investigation using two data-driven approaches.<n>A Random Forest classifier was developed to predict illness categories using waste disposal practices and demographic survey data.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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