Predictive analytics is revolutionizing occupational health and safety (OHS). It offers evidence-based insights. These insights enable proactive risk management and informed, data-driven decision-making in organizational settings. This paper explores the key components of predictive analytics in OHS, beginning with data collection, management, and preparation, and moving through to advanced predictive modelling techniques. We emphasize the importance of data integrity through processes such as missing value imputation, anomaly detection, and feature engineering to ensure accurate model predictions. Risk prioritization identifies and ranks hazards across various factors, including employee behaviours, organizational policies, environmental conditions, and operational practices. We posit that insights derived from predictive models must be effectively interpreted and implemented. These insights guide organizations to focus on high-impact areas for accident prevention and resource optimization. The integration of predictive analytics in OHS brings notable benefits, including enhanced decision-making, greater operational efficiency, cost savings, and improved compliance with safety standards. We examine applications of predictive analytics in OHS in Indian settings. India has the largest workforce in the world, and the predominance of it is in the informal sector - a sector largely unprotected by the already inadequate OHS laws. Ethical considerations, data privacy concerns, and the risk of overdependence on predictive models are discussed. We conclude with a discussion on the potential for predictive analytics to create a data-oriented, adaptive approach to OHS in India. We posit that, using predictive analytics, India can develop high safety standards while traversing the complexities of its workforce setting.

翻译：预测分析正在革新职业健康与安全领域。它提供基于证据的洞察，使组织能够主动进行风险管理并做出基于数据的明智决策。本文探讨了预测分析在职业健康与安全中的关键组成部分，从数据收集、管理和准备开始，逐步深入到先进的预测建模技术。我们强调通过缺失值填补、异常检测和特征工程等过程确保数据完整性，以实现准确的模型预测。风险优先级排序可识别并基于多种因素对危害进行分级，包括员工行为、组织政策、环境条件和操作实践。我们认为，从预测模型中获得的洞察必须得到有效解释和实施。这些洞察指导组织将重点放在事故预防和资源优化的高影响力领域。将预测分析整合到职业健康与安全中带来了显著益处，包括增强决策能力、提高运营效率、节约成本以及更好地遵守安全标准。我们研究了预测分析在印度职业健康与安全环境中的应用。印度拥有世界上最大的劳动力队伍，且其中大部分属于非正规部门——该部门在很大程度上未受本已不足的职业健康与安全法律保护。本文讨论了伦理考量、数据隐私问题以及对预测模型过度依赖的风险。最后，我们探讨了预测分析在印度创建一种数据导向、适应性强的职业健康与安全方法的潜力。我们认为，通过运用预测分析，印度可以在应对其复杂劳动力环境挑战的同时，发展高水平的安全标准。