A Resampling Method to Improve the Prognostic Model of End-Stage Kidney Disease: A Better Strategy for Imbalanced Data

Frontiers in Medicine

Tingyu Qu¹  Bart De Moor²  Gijs Van Pottelbergh³  Marjan van den Akker⁴  Xi Shi⁵ 

[1] Department of Computer Science, KU Leuven, Leuven, Belgium;Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium;Department of Public Health and Primary Care, Academic Centre of General Practice, KU Leuven, Leuven, Belgium;Institute of General Practice, Goethe University, Frankfurt am Main, Germany;Vlerick Business School, Leuven, Belgium;

关键词: logistic regression;    machine learning;    resampling method;    predictive performance;    chronic disease;   

DOI  :  10.3389/fmed.2022.730748

来源: DOAJ

【 摘 要 】

BackgroundPrognostic 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.MethodsThe 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.ResultsThe 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.ConclusionThis 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.

【 授权许可】

Unknown