Background: Metabolic syndrome (MS) screening is important for the early detection of occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.
Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. Finally, the screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers.
Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk factors were basophil absolute count (OR: 3.38), platelet packed volume (OR: 2.63), leukocyte count (OR: 2.01), red blood cell count (OR: 1.99), and alanine aminotransferase level (OR: 1.53).
Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify Metabolic syndrome with high accuracy in physically examining an occupational population.