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Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm
- Source :
- Frontiers in Public Health, Vol 9 (2021), Frontiers in Public Health
- Publication Year :
- 2021
- Publisher :
- Frontiers Media S.A., 2021.
-
Abstract
- Aim: Metabolic syndrome (MS) screening is essential for the early detection of the 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. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.
- Subjects :
- Population
Physical examination
Logistic regression
physical examination
metabolic syndrome
Lasso (statistics)
occupational population
Humans
Medicine
education
Original Research
education.field_of_study
medicine.diagnostic_test
lasso regression algorithm
business.industry
Public Health, Environmental and Occupational Health
Area under the curve
biomarkers
Odds ratio
Nomogram
Nomograms
Logistic Models
Public Health
Public aspects of medicine
RA1-1270
business
Risk assessment
Algorithm
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 22962565
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- Frontiers in Public Health
- Accession number :
- edsair.doi.dedup.....00382fd36c66e48cd8f5ba4375a27d0d
- Full Text :
- https://doi.org/10.3389/fpubh.2021.743731/full