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Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Prediction of kidney disease stages using data mining algorithms
inferredcites →https://doi.org/10.1016/j.kint.2023.10.018openalex-snapshot
KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease
inferredcites →https://doi.org/10.1016/j.knosys.2011.06.013openalex-snapshot
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
inferredcites →https://doi.org/10.1016/j.patcog.2013.05.006openalex-snapshot
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
inferredcites →https://doi.org/10.1016/j.patcog.2016.06.013openalex-snapshot
Dual autoencoders features for imbalance classification problem
inferredcites →https://doi.org/10.1016/s0140-6736(08)60952-6openalex-snapshot
All-cause mortality attributable to chronic kidney disease: a prospective cohort study based on 462 293 adults in Taiwan
inferredcites →https://doi.org/10.1038/ki.2010.536openalex-snapshot
Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts
inferredcites →https://doi.org/10.1038/ki.2013.243openalex-snapshot
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Logistic Regression in Rare Events Data
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The random subspace method for constructing decision forests
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An imbalanced data classification algorithm of improved autoencoder neural network
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Random decision forests
inferredcites →https://doi.org/10.1109/icmlc.2018.8526934openalex-snapshot
Imbalanced Data Classification Based on Extreme Learning Machine Autoencoder
inferredcites →https://doi.org/10.1109/ssci.2017.8285168openalex-snapshot
Variational autoencoder based synthetic data generation for imbalanced learning
inferredcites →https://doi.org/10.1109/tkde.2008.239openalex-snapshot
Learning from Imbalanced Data
inferredcites →https://doi.org/10.1109/tkde.2009.187openalex-snapshot
Combating the Small Sample Class Imbalance Problem Using Feature Selection
inferredcites →https://doi.org/10.1142/s0218213009000135openalex-snapshot
SYMBOLIC ONE-CLASS LEARNING FROM IMBALANCED DATASETS: APPLICATION IN MEDICAL DIAGNOSIS
inferredcites →https://doi.org/10.1155/2013/127962openalex-snapshot
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A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis
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Management of Chronic Kidney Disease Patients in the Intensive Care Unit: Mixing Acute and Chronic Illness
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Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes
inferredcites →https://doi.org/10.1186/s12882-017-0497-6openalex-snapshot
The cost-effectiveness of using chronic kidney disease risk scores to screen for early-stage chronic kidney disease
inferredcites →https://doi.org/10.1371/journal.pone.0158765openalex-snapshot
Global Prevalence of Chronic Kidney Disease – A Systematic Review and Meta-Analysis
inferredcites →https://doi.org/10.4135/9781412961288.n40openalex-snapshot
Categorical Data Analysis
inferredcites →https://doi.org/10.7326/0003-4819-158-11-201306040-00007openalex-snapshot
Evaluation and Management of Chronic Kidney Disease: Synopsis of the Kidney Disease: Improving Global Outcomes 2012 Clinical Practice Guideline

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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