Background: The present study aims was to investigate the effects of low and continuous radiation on hematological parameters and morphology in human blood tests. Besides, we know that machine learning methods can be utilized to evaluate the effect of dose absorbed by individual’s overtime on hematological parameters. In these methods, using available data, a model is designed to predict the desired variable and the accuracy of the model is assessed. Designing a meticulous prediction model signifies the relevance of available data and the desired variable. Therefore, this study explores the identified relationships using multi-layer perceptron modeling.
Methods: This is a cross-sectional study in which subjects with more than 2-year experience of working in Cath Lab who have been exposed to continuous radiation are included in the study.
Results: More than 58% of participants were female with a mean age of 39.8 ± 6.6 years and the rest were male with a mean age of 41.41 ± 7.98 years. In the initial studies and also the modeling of regression, no correlation was found between the exposure dose and the MCHC parameter, but neural network modeling indicated a nonlinear relationship. Also, despite initial studies that exhibited a relationship between the RBC parameter and the data analyzed, the regression model was not accurate for this parameter; however, the accuracy of the multi-layered perceptron model was desirable. This indicates a nonlinear relationship between the exposure doses of subjects and the RBC parameter.
Conclusion: According to the results of the study, hematological parameters are not reliable tests for assessing biological risks in long-term exposure. Therefore, for more accurate results, studies with larger sample size and longer duration are required.