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Exploring the Impact of Machine Learning on Predictive Safety Analytics: A Qualitative Study of Implementation Challenges and Benefits Across Key Industries

Year: 2025

Type: article

Abstract: This study explores the impact of machine learning (ML) on predictive safety analytics, focusing onimplementation challenges and benefits across key industries. Utilizing a qualitative research methodology,including interviews and case studies, the research identifies critical themes such as integration challengeswith legacy systems, data quality and management, regulatory compliance, and privacy concerns. The studyalso highlights significant benefits including enhanced predictive accuracy, operational efficiency, andimproved safety outcomes. The findings provide valuable insights into the successful adoption of MLtechnologies for safety analytics, emphasizing the importance of cross-functional collaboration and effectivechange management.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16761026

Author: Takfarinas Ben Youcef

Language: English

Topic: Occupational Health and Safety Research

Subfield: Radiological and Ultrasound Technology

Field: Health Professions

Domain: Health Sciences

SDGs: Good health and well-being, Industry, innovation and infrastructure, Decent work and economic growth

Open Access status: green

OpenAlex ID: W6949745278

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