Classifying smoking status using linear and non-linear models based on clinical health records
Murilo de Oliveira Souza1,2, Paulo Roberto Filgueiras3, Ian Wilson4,5
1Laboratory of Analytics, Metabolomics, and Chemometrics, Federal Institute of Espírito Santo, ES-482 Cachoeiro-Alegre, Km 72 - Rive, Alegre, ES, 29500-000, Brazil. murilo.souza@ifes.edu.br.
Analytical and Bioanalytical Chemistry
|June 10, 2026
Summary
Routine clinical chemistry data can identify smoking habits using multivariate analysis. Random forest models effectively distinguished smokers from non-smokers, highlighting key biochemical differences.
Area of Science:
- Biochemistry
- Data Science
- Public Health
Background:
- Routine clinical chemistry data are underutilized for lifestyle characterization.
- Multivariate analysis offers a cost-effective method for population-level lifestyle pattern identification.
Purpose of the Study:
- To evaluate linear and non-linear multivariate methods for discriminating smokers and non-smokers using routine clinical chemistry data.
- To identify key biochemical markers associated with smoking status.
Main Methods:
- Compared linear (Partial Least Squares-Discriminant Analysis, Logistic Regression) and non-linear (Support Vector Machines, Random Forest) classification models.
- Utilized 23 routine clinical chemistry measurements to differentiate between smokers and non-smokers.
- Performed variable importance analysis to identify key discriminating markers.
Main Results:
- Random forest demonstrated superior classification performance compared to linear methods.
- Key discriminating variables included cholesterol ratio, total protein, potassium, and lactate dehydrogenase.
- Identified significant metabolic and physiological differences between smokers and non-smokers.
Conclusions:
- Routinely available clinical chemistry parameters can effectively capture smoking-related biochemical alterations.
- Multivariate analysis of standard lab tests supports lifestyle stratification.
- This approach offers a practical screening strategy complementary to metabolomic studies.
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