Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Reliable prediction of traumatic brain injury (TBI) outcomes based on machine learning (ML) that is derived from quantitative electroencephalography (EEG) features has renewed interest in recent years. Nevertheless, the approach has suffered from imbalanced datasets. Hence, to get a reliable predict...

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Bibliographic Details
Main Authors: Nor Safira Elaina Mohd Noor, Haidi Ibrahim, Muhammad Hanif Che Lah, Jafri Malin Abdullah
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Boosting
boosting sampling
class imbalance
electroencephalography
ensemble model
machine learning
Online Access:https://ieeexplore.ieee.org/document/9527215/

Internet

https://ieeexplore.ieee.org/document/9527215/

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