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Machine learning models tackle waste management and health risks in Ghana

By PulseAugur Editorial · [1 sources] ·

Researchers have developed machine learning models to address waste management and health risks in Ghana. A Random Forest classifier was trained on survey data to predict illness categories based on waste disposal practices, achieving a macro F1 score of 0.63. Additionally, a MobileNetV2 model was used for automated waste sorting, reaching 88.2% accuracy and a macro F1 score of 0.87, offering a cost-effective solution for resource-constrained areas. The study provides quantitative evidence linking waste disposal to health and demonstrates the potential of AI in improving waste management in developing regions, while also highlighting the need for institutional support. AI

IMPACT Demonstrates the application of machine learning for public health and waste management in resource-constrained environments.

RANK_REASON The cluster contains an academic paper detailing novel research findings and model development. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning models tackle waste management and health risks in Ghana

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The cluster contains an academic paper detailing novel research findings and model development. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hilda Adwubi Osei, Catherine Tenewaa Osei, Desdemona Yaa Asobayire ·

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

    arXiv:2608.25759v1 Announce Type: new 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 …

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