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

This research explores the application of machine learning (ML) to address the public health and environmental challenges posed by solid waste disposal in Ghana. It validates a community-perceived link between waste disposal and illness patterns using predictive modeling and proposes computer vision for waste sorting to improve management practices. The study highlights the potential of data-driven approaches to inform policy and operational strategies for waste management and public health.

Why it matters

This research provides quantitative validation for the link between waste management practices and public health outcomes, offering a data-driven basis for strategic interventions. It demonstrates the tangible benefits of integrating advanced technological solutions like machine learning and computer vision into environmental and public health strategies, which can lead to more effective resource allocation and improved societal well-being.

Key insights

• Machine learning, specifically a Random Forest classifier, can predict illness categories based on waste disposal practices and demographic data, validating a community-perceived link.
• The predictive model achieved an Area Under the Curve (AUC) of 0.82 for predicting illness categories, demonstrating its efficacy in identifying health risks associated with waste.
• Computer vision (YOLOv8) models can classify common household waste types (organic, plastic, paper, metal, glass, fabric) with high accuracy, indicating potential for automated sorting.
• The most accurate YOLOv8 model achieved a mean Average Precision (mAP) of 0.707 over 6 classes, suggesting operational viability for waste segregation.
• Inappropriate solid waste disposal is a significant public health and environmental concern globally, with substantial economic costs and avoidable deaths.
• A 2022 field study in Atonsu, Kumasi, Ghana, identified a community-perceived relationship between waste disposal and illness patterns, which this study quantitatively validates.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.25759