Abstract
Prediction models that rely on time series data are often affected by diminished predictive accuracy. This occurs from the causal relationships of the data that shift over time. Thus, the changing weights that are used to create prediction models lose their informational value. One way to correct this change is by using concept drift information. That is exactly what prediction models in biomedical applications need. Currently, metabolomics is at the forefront in modeling analysis for phenotype prediction, making it one of the most interesting candidates for biomedical prediction diagnosis. However, metabolomics datasets include dynamic information that can harm prediction modeling. The study presents concept drift correction methods to account for dynamic changes that occur in metabolomics data for better prediction outcomes of phenotypes in a biomedical setting.
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Acknowledgement
This work has been supported by grant FEKT-K-21-6878 realised within the project Quality Internal Grants of BUT (KInG BUT), Reg. No. CZ.02.2.69/0.0 /0.0/19_073/0016948, which is financed from the OP RDE.
We would like to thank Adam Hospodka for their support of our study, by building on the project team’s results in Machine Learning and Data Mining (PV056) course at Masaryk University.
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Schwarzerova, J. et al. (2022). A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2022. Advances in Intelligent Systems and Computing, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-031-09135-3_42
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DOI: https://doi.org/10.1007/978-3-031-09135-3_42
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