Abstract

Occupational diseases remain a significant challenge in high-risk industries, where hazardous working conditions expose employees to health risks that often go undetected until symptoms become severe. To address this, leveraging artificial intelligence (AI) and machine learning (ML) offers transformative potential for proactive health risk management by enabling predictive modeling, real-time monitoring, and data-driven decision-making. This study presents a conceptual framework for integrating AI and ML technologies to predict and mitigate occupational diseases in high-risk industries such as mining, construction, and manufacturing. The proposed framework encompasses three key components: data acquisition, predictive modeling, and intervention strategies. Data acquisition involves collecting real-time health and environmental data through wearable sensors, IoT-enabled devices, and workplace monitoring systems. Predictive modeling employs advanced ML algorithms, such as decision trees, neural networks, and support vector machines, to identify patterns and risk factors associated with occupational diseases. Intervention strategies leverage predictive insights to develop targeted prevention measures, such as redesigning work environments, optimizing workflows, and implementing personalized health interventions. A case study approach evaluates the framework’s applicability, focusing on high-risk industries in Nigeria. Initial results demonstrate the feasibility of using AI-driven systems to identify early indicators of diseases such as respiratory disorders, musculoskeletal conditions, and noise-induced hearing loss. The findings also highlight the framework's potential to enhance workplace safety, reduce healthcare costs, and improve employee well-being by transitioning from reactive to proactive health management. The framework underscores the importance of cross-disciplinary collaboration among engineers, healthcare professionals, and policymakers to ensure effective implementation. Ethical considerations, such as data privacy and fairness, are also addressed to ensure equitable access and compliance with international health and safety standards. This conceptual framework lays the foundation for future research and policy development aimed at integrating AI and ML technologies into occupational health systems, particularly in resource-constrained settings, to foster safer and healthier work environments.