cs.LGAug 26, 2026

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

Authors: Hilda Adwubi Osei, Catherine Tenewaa Osei, Desdemona Yaa Asobayire

Organizations: Department of Industrial Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana · Department of Nursing Kwame Nkrumah University of Science and Technology Kumasi Ghana · Department of Mechanical Engineering University of Nottingham NG7 2RD, United Kingdom

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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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Supplementary material from the paper’s appendix.

Appendix

Figure 1: Top 15 feature importances for the final Random Forest survey model, showing fell_sick_Yes and fell_sick_No dominating, followed by the four disposal-method indicator features in descending order (see Section 5.1 ).
Figure 2: Confusion matrix for the survey predictive model on the full held-out test set ( N=94N=94 ), covering all five categories including “None reported” (see Section 5.1 ).
Figure 3: Confusion matrix for the image classification model on the held-out test set ( N=415N=415 ), covering all six TrashNet waste categories (see Section 5.2 ).

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