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    A resampling method to improve the prognostic model of end-stage kidney disease: A better strategy for imbalanced data

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    Publication type
    Journal article with impact factor
    Author
    Shi, Xi
    Qu, Tingyu
    Van Pottelbergh, Gijs
    van den Akker, Marjan
    De Moor, Bart
    Publication Year
    2022
    Journal
    Frontiers in Medicine
    
    Metadata
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    Abstract
    Background: Prognostic models can help to identify patients at risk for end-stage kidney disease (ESKD) at an earlier stage to provide preventive medical interventions. Previous studies mostly applied the Cox proportional hazards model. The aim of this study is to present a resampling method, which can deal with imbalanced data structure for the prognostic model and help to improve predictive performance. Methods: The electronic health records of patients with chronic kidney disease (CKD) older than 50 years during 2005–2015 collected from primary care in Belgium were used (n = 11,645). Both the Cox proportional hazards model and the logistic regression analysis were applied as reference model. Then, the resampling method, the Synthetic Minority Over-Sampling Technique-Edited Nearest Neighbor (SMOTE-ENN), was applied as a preprocessing procedure followed by the logistic regression analysis. The performance was evaluated by accuracy, the area under the curve (AUC), confusion matrix, and F3 score. Results: The C statistics for the Cox proportional hazards model was 0.807, while the AUC for the logistic regression analysis was 0.700, both on a comparable level to previous studies. With the model trained on the resampled set, 86.3% of patients with ESKD were correctly identified, although it was at the cost of the high misclassification rate of negative cases. The F3 score was 0.245, much higher than 0.043 for the logistic regression analysis and 0.022 for the Cox proportional hazards model. Conclusion: This study pointed out the imbalanced data structure and its effects on prediction accuracy, which were not thoroughly discussed in previous studies. We were able to identify patients with high risk for ESKD better from a clinical perspective by using the resampling method. But, it has the limitation of the high misclassification of negative cases. The technique can be widely used in other clinical topics when imbalanced data structure should be considered.
    Keyword
    Logistic Regression, Resampling Method, Predictive Performance, Chronic Diseases
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    DOI
    10.3389/fmed.2022.730748
    URI
    http://hdl.handle.net/20.500.12127/7012
    ae974a485f413a2113503eed53cd6c53
    10.3389/fmed.2022.730748
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