Biostatistics

A Support Vector Regression Approach for Three–Level Longitudinal Data: A Simulation Study


Abstract


Background: Longitudinal data structure is frequently observed in health science. This introduces correlation to the data that needs to be handled in modeling process. Recently, machine learning approaches are performed in longitudinal data for prediction of the response variable. In this paper a mixed-effects least squares support vector regression model is presented for three-level longitudinal data. In the proposed model, multiple random effect terms are used for considering the existing correlation structures in longitudinal data. The proposed model is flexible in modeling (non)linear and complex relationships between predictors and response, while it takes into account the hierarchical structure of the data and is computationally efficient. Methods: Both random intercept and random trend models with a special correlation structure of errors are illustrated. A real data example on human Brucellosis rate is analyzed and two simulation studies are performed to illustrate the proposed model. The fitting and generalization performance of the proposed model are investigated and compared with the ordinary least squares support vector regression and linear mixed-effects models. Results: Based on the human Brucellosis rate example and two simulation studies, the proposed models had the best performance in generalization. Also, the fitting performance of the proposed models was better than classic models. Conclusion: Our study revealed that in presence of nonlinear relationship between covariates and outcome, the proposed MLS-SVR models have the best fitting and generalization performance.


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DOI: https://doi.org/10.2427/13129

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EBPH Epidemiology, Biostatistics and Public Health | ISSN 2282-0930

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.