Learning from waste: Machine Learning for health risk prediction and computer vision-based sorting in Ghana
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 develope
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“Fuzzy title match (0.73): “Learning from waste: Machine Learning for health risk predic” ≈ “VioletVision-3B””
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- PossiblePossibly related (embedding) · 46%Operational evidence standards for machine learning in wastewater treatment - Nature →
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
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- FuzzySimilar title/name (fuzzy) · 66%DataTalksClub/machine-learning-zoomcamp →
“Fuzzy title match (0.78): “Learning from waste: Machine Learning for health risk predic” ≈ “DataTalksClub/machine-learning-zoomcamp””
- FuzzySimilar title/name (fuzzy) · 66%stefan-jansen/machine-learning-for-trading →
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- FuzzySimilar title/name (fuzzy) · 59%EthicalML/awesome-production-machine-learning →
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