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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
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Predicting dichotomised outcomes from high-dimensional data in biomedicine
Armin Rauschenberger1, Enrico Glaab1
1Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
Journal of Applied Statistics
|June 27, 2024
Summary
This study introduces a new statistical method to improve binary outcome predictions in biomedical research. By combining logistic and linear regression, it enhances prediction accuracy for dichotomized outcomes, avoiding information loss from numerical data.
Area of Science:
- Biostatistics
- Biomedical Informatics
- Machine Learning in Healthcare
Background:
- Biomedical research often requires predicting probabilities of outcomes exceeding a threshold (e.g., immunity, disease severity).
- Converting numerical outcomes to binary simplifies analysis but causes significant information loss.
- Existing logistic regression models for binary outcomes do not fully leverage available numerical data.
Purpose of the Study:
- To develop a statistical approach that improves prediction of dichotomized outcomes by integrating both numerical and binary data.
- To address the limitations of converting numerical outcomes to binary, thereby preserving information and enhancing predictive power.
- To provide a robust method for high-dimensional data analysis in biomedical applications.
Main Methods:
- A novel approach combining logistic regression for binary outcomes and linear regression for numerical outcomes.
- Transformation of predicted values from linear regression into predicted probabilities.
- Integration of predicted probabilities from both models to improve overall classification accuracy.
- Analysis of high-dimensional simulated and experimental (clinical) data.
Main Results:
- Significantly improved predictions of dichotomized outcomes were achieved using the proposed combined approach.
- The method effectively leverages both numerical and binary data, outperforming traditional methods that discard information.
- Demonstrated superior performance on high-dimensional clinical data for predicting cognitive impairment.
Conclusions:
- The proposed statistical method offers a powerful way to combine binary and numerical outcomes for enhanced binary classification.
- This approach mitigates information loss inherent in traditional data conversion methods, leading to more accurate biomedical predictions.
- The R package 'cornet' is available for implementing this advanced statistical technique in high-dimensional settings.
Keywords:
62-0462H3062J0562J0762P10Linear/logistic regressionbinary classificationdichotomisationhigh-dimensional datanumerical predictionridge/lasso regularisationMore Related Videos
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