Birth Mode Prediction Using Bagging Ensemble Classifier: A Case Study of Bangladesh

Alam, Md. Sakib Bin; Patwary, Muhammed J. A.; Hassan, Maruf

doi:10.1109/icict4sd50815.2021.9396909

Cited by 30 publications

(10 citation statements)

References 25 publications

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“…Ensemble models show better performance than individual models, so they have been adopted for pregnancy health risk prediction as well. An ensemble classifiers-based [29] approach is utilized to predict the birth mode in this study [30]. The study provides the proper identification of health risk levels accomplice with pregnant woman delivery and helps reduce the mortality rate in Bangladesh.…”

Section: Related Workmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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Maternal health is an important aspect of women’s health during pregnancy, childbirth, and the postpartum period. Specifically, during pregnancy, different health factors like age, blood disorders, heart rate, etc. can lead to pregnancy complications. Detecting such health factors can alleviate the risk of pregnancy-related complications. This study aims to develop an artificial neural network-based system for predicting maternal health risks using health data records. A novel deep neural network architecture, DT-BiLTCN is proposed that uses decision trees, a bidirectional long short-term memory network, and a temporal convolutional network. Experiments involve using a dataset of 1218 samples collected from maternal health care, hospitals, and community clinics using the IoT-based risk monitoring system. Class imbalance is resolved using the synthetic minority oversampling technique. DT-BiLTCN provides a feature set to obtain high accuracy results which in this case are provided by the support vector machine with a 98% accuracy. Maternal health exploratory data analysis reveals that the health conditions which are the strongest indications of health risk during pregnancy are diastolic and systolic blood pressure, heart rate, and age of pregnant women. Using the proposed model, timely prediction of health risks associated with pregnant women can be made thus mitigating the risk of health complications which helps to save lives.

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Section: Related Workmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…To obtain insights, the authors used the LIME and SHAP interpretability for the classification. Alam, Patwary & Hassan (2021) proposed a bagging ensemble model for the prediction of birth mode in Bangladeshi women. k-NN, DT, and SVM are implemented separately, as well as, with the bagging ensemble.…”

Section: Related Workmentioning

confidence: 99%

Improving prediction of maternal health risks using PCA features and TreeNet model

Jamel,

Umer,

Saidani

et al. 2024

PeerJ Computer Science

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Maternal healthcare is a critical aspect of public health that focuses on the well-being of pregnant women before, during, and after childbirth. It encompasses a range of services aimed at ensuring the optimal health of both the mother and the developing fetus. During pregnancy and in the postpartum period, the mother’s health is susceptible to several complications and risks, and timely detection of such risks can play a vital role in women’s safety. This study proposes an approach to predict risks associated with maternal health. The first step of the approach involves utilizing principal component analysis (PCA) to extract significant features from the dataset. Following that, this study employs a stacked ensemble voting classifier which combines one machine learning and one deep learning model to achieve high performance. The performance of the proposed approach is compared to six machine learning algorithms and one deep learning algorithm. Two scenarios are considered for the experiments: one utilizing all features and the other using PCA features. By utilizing PCA-based features, the proposed model achieves an accuracy of 98.25%, precision of 99.17%, recall of 99.16%, and an F1 score of 99.16%. The effectiveness of the proposed model is further confirmed by comparing it to existing state of-the-art approaches.

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“…In the same way, Alan et al [25] employed bagging ensemble classifiers for predicting birth modes such as cesarean section or normal delivery, an innovative strategy for predicting birth modes. Experimental results revealed that bagging ensembles outclass the single classifiers in this classification task.…”

Section: Background and Related Workmentioning

confidence: 99%

An Optimized Bagging Ensemble Learning Approach Using BESTrees for Predicting Students’ Performance

Evangelista

2023

Int. J. Emerg. Technol. Learn.

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Every academic institution's goal is to identify students who require additional assistance and take appropriate actions to improve their performance. As such, various research studies have focused on developing prediction models that can detect correlated patterns influencing students' performance, dropout, collaboration, and engagement. Among the influential predictive models available, the bagging ensemble has captured the interest of researchers seeking to improve prediction accuracy over single classifiers. However, prior work in this area has focused mainly on selecting single classifiers as the base classifier of the bagging ensemble, with little to no further optimization of the proposed framework. This study aims to fill this gap by providing a bagging ensemble framework to optimize its hyperparameters and achieve improved prediction accuracy. The proposed model used the Weka BESTrees data mining tool and Math language course student dataset from UCI Machine Learning Repository. Based on the experiments performed, the proposed bagging optimization technique can effectively increase the accuracy of a traditional bagging ensemble method. It reveals further that the proposed BESTrees framework can achieve an optimized performance when trained with the appropriate hyperparameters and hill climb metrics.

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Birth Mode Prediction Using Bagging Ensemble Classifier: A Case Study of Bangladesh

Cited by 30 publications

References 25 publications

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Improving prediction of maternal health risks using PCA features and TreeNet model

An Optimized Bagging Ensemble Learning Approach Using BESTrees for Predicting Students’ Performance

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