Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data

Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studi...

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Bibliographic Details
Main Authors: Diago-Almela, V.J (Author), Diaz-Martinez, A. (Author), Garcia-Casado, J. (Author), Hao, D. (Author), Monfort-Ortiz, R. (Author), Nieto-Del-amor, F. (Author), Prats-Boluda, G. (Author), Ye-Lin, Y. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Classification (of information)
electrohysterography
Electrohysterography
Electrophysiology
Feature Selection
Feature selection methods
Forecasting
genetic algorithm
Genetic algorithms
imbalance data learning
Imbalance data learning
Imbalance datum
machine learning
Machine-learning
Preterm labor
preterm labor prediction
Preterm labor prediction
Resampling method
resampling methods
Statistical tests
Under-sampling
uterine electromyography
Uterine electromyography
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