Russian researchers have developed a new program that allows pre-assessment of individual occupational health risks for workers in hazardous industries. The development was created by specialists from Perm Polytechnic University in collaboration with Rospotrebnadzor and the Federal Research Center for Medical and Preventive Technologies for Public Health Risk Management. The results are published in the journal “Health Risk Analysis”.
In Russia, thousands of jobs are classified as harmful and dangerous: mines, metallurgy, aircraft manufacturing, mining. At such enterprises, employees have been working for years in conditions of noise, vibration, dust and exposure to chemicals. This leads to an increase in occupational diseases, from hearing loss to chronic lung diseases.
The main problem with existing risk assessment methods is their averaging. They analyze groups of workers, not a specific person, and do not allow predicting diseases in advance. Medical examinations record violations that have already occurred, but do not prevent them.
The new program solves this problem. It uses an adaptive neuro‑fuzzy network capable of working with incomplete and blurred data. The system analyzes the working conditions, length of service, age and medical indicators of a particular employee, after which it calculates an individual risk index for the development of the disease. The forecast accuracy is 87-89%, which is confirmed by testing on real data from underground mining workers and drillers.
The development was trained on a unique database of 175 thousand parameters: noise and vibration levels, concentrations of harmful substances, test results and actual diagnoses. The program automatically identifies complex relationships between factors and generates a personal forecast, which is displayed as a clear risk category — from minimal to very high.
According to the developers, the system allows:
— identify workers with increased risks even before symptoms appear;
— prescribe personal preventive measures;
— make targeted decisions to improve working conditions;
— analyze the “pain points” at the workshop or enterprise level.
The technology is scalable and can be adapted to any industry with harmful working conditions, from mining to mechanical engineering. This makes it an effective tool for early detection of risks, preserving the health of employees and making informed decisions to improve the production environment.