ArticleJournal of medical systems2018
Prediction of Hemodialysis Timing Based on LVW Feature Selection and Ensemble Learning.
Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records.IEEE journal of translational engineering in health and medicine · 2023Article
- Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis.Annals of medicine · 2023Article
- Early prediction of hemodialysis complications employing ensemble techniques.Biomedical engineering online · 2022Article
- Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination Techniques.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
Indexed as
Identifiers
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Registered trials
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