Abstract
We propose an improved model based on LVW embedded model feature extractor and ensemble learning for improving prediction accuracy of hemodialysis timing in this paper. Due to this drawback caused by feature extraction models, we adopt an enhanced LVW embedded model to search the feature subset by stochastic strategy, which can find the best feature combination that are most beneficial to learner performance. In the model application, we present an improved integrated learners for model fusion to reduce errors caused by overfitting problem of the single classifier. We run several state-of-the-art Q&A methods as contrastive experiments. The experimental results show that the ensemble learning model based on LVW has better generalization ability (97.04%) and lower standard error (± 0.04). We adopt the model to make high-precision predictions of hemodialysis timing, and the experimental results have shown that our framework significantly outperforms several strong baselines. Our model provides strong clinical decision support for physician diagnosis and has important clinical implications.
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Funding
This study was funded by the Science and Technology Plan Project of Sichuan Province under Grant 2016GZ0092.
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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
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This article is part of the Topical Collection on Systems-Level Quality Improvement
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Xiong, Cz., Su, M., Jiang, Z. et al. Prediction of Hemodialysis Timing Based on LVW Feature Selection and Ensemble Learning. J Med Syst 43, 18 (2019). https://doi.org/10.1007/s10916-018-1136-x
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DOI: https://doi.org/10.1007/s10916-018-1136-x