International Journal of Biosafety, Biosecurity & Bioscience Innovations
E-ISSN: 3027-0235
Volume 2&3, No. 1, 2025
Pages 1-22
DOI: 10.36108/ijbbb/5202.320.0110
Leveraging Machine Learning to Assess Secondary Contamination Risks in Laboratory Waste Systems: A Biosafety Perspective
*OLU Joshua1, MKPUMAH Emmanuel Ifeanyi2, LAWRENCE Morolake Oladayo3, OGUNMEFUN Gbenga Samson4, NWOSU-EZEONYE Priscilla Odaku5, EBERECHUKWU Happiness Oluomachi6, YUSUF Hajara Oyiza5
1 HONA Tech Ltd. FCT, Abuja
2 Art and Creative Technologies, Faculty of Engineering, School of Engineering, University of Bolton
3 Department of Software Engineering, Miva Open University, Abuja, Nigeria
4 Technology and Innovation Support Centre under the DG/CEO, National Biotechnology Research and Development Agency, Umaru Musa Yar’adua Expressway, Lugbe, Abuja – Nigeria
5 Bio entrepreneurship and Consultancy Services Department, National Biotechnology Research and Development Agency, Umaru Musa Yar’adua Expressway, Lugbe, Abuja – Nigeria
6 Bioresources Development Centre (BioDeC), Okwudor, National Biotechnology Research and Development Agency, Umaru Musa Yar’adua Expressway, Lugbe, Abuja – Nigeria
*Corresponding Author- Email: olu@lujosh.com Orcid No: http://orcid,org/0000-0002-5865-3640
Abstract
This study investigates secondary contamination risks in laboratory waste handling and disposal systems, leveraging machine learning to assess risks and evaluate biosafety practices. The study employed a quantitative, cross-sectional design, surveying 351 laboratory personnel across institutional, clinical, and university labs utilizing a validated questionnaire (Cronbach’s α > 0.79) to evaluate waste characteristics, handling, treatment, disposal, biosafety training, and policy compliance. Data were analyzed using statistical methods (Friedman Test, Kruskal-Wallis, Spearman’s Rank Correlation) and machine learning models (Logistic Regression, Random Forest, XGBoost). Friedman tests revealed significant differences in perceived contamination risks (χ²(4) = 441.23, p < .001), with waste generation, improper storage, and infection potential as key drivers. K-means clustering identified three distinct waste handling patterns, strongly associated with personnel experience (χ²(8) = 495.16, p < .05). XGBoost regression (R² = 0.997) confirmed biosafety training’s critical role in effective waste treatment, with disposal facility inspections and regulatory compliance as top predictors. Logistic regression showed a significant association between institutional policy compliance and biosafety awareness (β = 0.493, p = .002), though classification accuracy was limited (53.9%). Random Forest models (96.66% variance explained) validated these findings, emphasizing robust waste management protocols. In conclusion, these findings underscore the need for targeted training and stringent compliance to mitigate secondary contamination in laboratory settings, offering a scalable ML framework for biosafety risk assessment.
Keywords: Biosafety, Laboratory Waste, Machine Learning, Secondary Contamination, Risk Assessment, Waste Management
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