Manufacturing industries (Mrg-Inds) entails physically transformation of goods, with activities such as molding, cutting, and assembly, to final product. Hazards, associated with the activities in Mrg-Inds, are common among its workers. All the industries are face with tailored hazard risks and it is difficult to provide a common corrective measure. This study aimed at finding the possibility of integrating customized hazards in food, textile, chemical, leather and shoe, furniture and wood, and metal Mrg-Inds. The six industries assessed were located in Lagos and Ibadan, the southwestern part of Nigeria. Data were collected from 300 workers through questionnaire. Information related to peculiar types and prevalence of hazards in each of the industries were collected. Machine learning decision tree (Mld-tree) device, implemented in SPSS, was used to recognize the common hazards requiring the same decreasing measures. More than twenty-six hazards were collated from all the study areas. Chemical-related was rated (96.7%) major across the industries. This was followed by machinery-related (90%) and slips/falls (89%). The Mld-tree spotted hazards related with ‘equipment’ and ‘ergonomics’ as the first chance node and the most common across all the industries. The chance node was ‘noise’ derived when hazards related with ‘equipment’ was further splatted into end node. The study identified hazards connected with ‘machinery’, ‘ergonomics’ and ‘noise’ as the integrated hazards prevalent in all the six Mrg-Inds that require common ergonomics intervention at reducing health and safety risks of workers in food, textile, chemical, leather and shoe, furniture and wood, and metal Mrg-Inds.
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