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Predicting medical waste generation and associated factors using machine learning in the Kingdom of Bahrain

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Abstract

Effective planning and managing medical waste necessitate a crucial focus on both the public and private healthcare sectors. This study uses machine learning techniques to estimate medical waste generation and identify associated factors in a representative private and a governmental hospital in Bahrain. Monthly data spanning from 2018 to 2022 for the private hospital and from 2019 to February 2023 for the governmental hospital was utilized. The ensemble voting regressor was determined as the best model for both datasets. The model of the governmental hospital is robust and successful in explaining 90.4% of the total variance.

Similarly, for the private hospital, the model variables are able to explain 91.7% of the total variance. For the governmental hospital, the significant features in predicting medical waste generation were found to be the number of inpatients, population, surgeries, and outpatients, in descending order of importance. In the case of the private hospital, the order of feature importance was the number of inpatients, deliveries, personal income, surgeries, and outpatients. These findings provide insights into the factors influencing medical waste generation in the studied hospitals and highlight the effectiveness of the ensemble voting regressor model in predicting medical waste quantities.

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Acknowledgements

Research Committee for Government Hospitals, Kingdom of Bahrain, through approval No. 57300522, is highly acknowledged. The management and dedicated officials of SMC and PH are highly acknowledged for their unwavering support and cooperation in providing essential data for this research.

The authors express their appreciation to Bahrain Polytechnic-BP for the support that made the publication of this article successful.

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Authors and Affiliations

  1. Environment and Sustainable Development, College of Science, University of Bahrain, Sakhir, 32038, Kingdom of Bahrain

    Khadija Al-Omran & Ezzat Khan

  2. School of Logistics and Maritime Studies, Faculty of Business and Logistics, Bahrain Polytechnic, Isa Town, 33349, Kingdom of Bahrain

    Khadija Al-Omran

  3. Department of Chemistry, University of Malakand, Lower Dir, Chakdara, 18800, Khyber Pakhtunkhwa, Pakistan

    Ezzat Khan

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  1. Khadija Al-Omran
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KA: conceptualization, writing–original draft, methodology, writing–review and editing; EK: conceptualization, supervision, writing–review and editing.

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Correspondence to Khadija Al-Omran.

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Al-Omran, K., Khan, E. Predicting medical waste generation and associated factors using machine learning in the Kingdom of Bahrain. Environ Sci Pollut Res 31, 38343–38357 (2024). https://doi.org/10.1007/s11356-024-33773-1

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