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Journal of Medical Systems

, 43:18 | Cite as

Prediction of Hemodialysis Timing Based on LVW Feature Selection and Ensemble Learning

  • Chang-zhu XiongEmail author
  • Minglian Su
  • Zitao Jiang
  • Wei Jiang
Systems-Level Quality Improvement
  • 141 Downloads
Part of the following topical collections:
  1. Artificial Intelligence Application in Health Informatics

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.

Keywords

Hemodialysis timing LVW Ensemble learning Prediction Feature selection Model fusion 

Notes

Funding

This study was funded by the Science and Technology Plan Project of Sichuan Province under Grant 2016GZ0092.

Compliance with ethical standards

Conflict of interest

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.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of electronic informationSichuan UniversityChengduChina
  2. 2.West China School of clinical medicineSichuan UniversityChengduChina

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