A new machine learning framework for occupational accidents forecasting with safety inspections integration

Reducing the number of occupational accidents remains a major challenge for companies, as these events lead to significant human harm and financial losses. Although many organizations have implemented safety programs and made continuous efforts to improve their prevention strategies, these measures often remain insufficient to proactively and dynamically anticipate risks. In particular, safety inspections are still largely underexploited, and their integration into continuously updated predictive models has received little attention.

We propose a model-agnostic framework for short-term occupational accident forecasting that leverages safety inspections and models accident occurrences as binary time series. The approach generates daily predictions, which are then aggregated into weekly safety assessments for better decision-making. To ensure the reliability and operational applicability of the forecasts, we apply a sliding-window cross-validation procedure specifically designed for time series data, combined with an evaluation based on aggregated period-level metrics. Several machine learning algorithms, including logistic regression, tree-based models, and neural networks, are trained and systematically compared within this framework.

Across all tested algorithms, the proposed framework reliably identifies upcoming high-risk periods and delivers robust period-level performance, demonstrating that converting safety inspections into binary time series yields actionable, short-term risk signals.

The proposed methodology converts routine safety inspection data into clear weekly and daily risk scores, detecting the periods when accidents are most likely to occur. Decision-makers can integrate these scores into their planning tools to classify inspection priorities, schedule targeted interventions, and funnel resources to the sites or shifts classified as highest risk, stepping in before incidents occur and getting the greatest return on safety investments.