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Article

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

1
Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
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Servicio de Obstetricia, H.U.P. La Fe, 46026 Valencia, Spain
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Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Academic Editors: Sidong Liu, Roneel V. Sharan and Hao Xiong
Sensors 2022, 22(14), 5098; https://doi.org/10.3390/s22145098
Received: 13 June 2022 / Revised: 1 July 2022 / Accepted: 5 July 2022 / Published: 7 July 2022
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 studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics. View Full-Text
Keywords: genetic algorithm; imbalance data learning; electrohysterography; preterm labor prediction; resampling methods; uterine electromyography; machine learning genetic algorithm; imbalance data learning; electrohysterography; preterm labor prediction; resampling methods; uterine electromyography; machine learning
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MDPI and ACS Style

Nieto-del-Amor, F.; Prats-Boluda, G.; Garcia-Casado, J.; Diaz-Martinez, A.; Diago-Almela, V.J.; Monfort-Ortiz, R.; Hao, D.; Ye-Lin, Y. Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data. Sensors 2022, 22, 5098. https://doi.org/10.3390/s22145098

AMA Style

Nieto-del-Amor F, Prats-Boluda G, Garcia-Casado J, Diaz-Martinez A, Diago-Almela VJ, Monfort-Ortiz R, Hao D, Ye-Lin Y. Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data. Sensors. 2022; 22(14):5098. https://doi.org/10.3390/s22145098

Chicago/Turabian Style

Nieto-del-Amor, Félix, Gema Prats-Boluda, Javier Garcia-Casado, Alba Diaz-Martinez, Vicente Jose Diago-Almela, Rogelio Monfort-Ortiz, Dongmei Hao, and Yiyao Ye-Lin. 2022. "Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data" Sensors 22, no. 14: 5098. https://doi.org/10.3390/s22145098

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