Improving the Performance of Classifiers by Ensemble Techniques for the Premature Finding of Unusual Birth Outcomes from Cardiotocography

Manikandan, M.; Vijayakumar, P.

doi:10.1080/03772063.2021.1910579

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…infant mortality in the early stages of pregnancy (42). They used ML models such as DT, NB, RF and KNN to classify the CTG dataset consisting of 2126 records into N, S and P cases.…”

Section: Manikandan Et Al Proposed Methods For Predictingmentioning

confidence: 99%

Artificial Intelligence in Fetal Health Diagnosis: A Systematic Literature Review

Kuzu,

Santur

2023

TUSEB

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Obstetricians commonly use Non-Stress Test to evaluate fetal well-being during the antepartum and intrapartum periods by measuring fetal heart rate and uterine contractions of the mother. Non-Stress Test is also used to diagnose fetal distress at an early stage. Early diagnosis and treatment can increase the fetus's survival rate and improve their quality of life. The fetal heart rate and uterine contraction signals obtained from the Non-Stress Test are recorded on a paper called a trace. Obstetricians interpret the trace to make decisions about the fetus's condition. However, traditional analysis of Non-Stress Test takes time, and there are differences in interpretation among experts. Newly qualified doctors and midwives are more prone to making mistakes and incorrect decisions. To overcome the differences in the interpretation of Non-Stress Test analysis and to automate the process to minimize diagnostic errors, machine learning and deep learning models have been increasingly used in recent years. In this study, the literature of the past five years is researched, and numerical expressions, tables, and graphs related to this are presented.

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“…infant mortality in the early stages of pregnancy (42). They used ML models such as DT, NB, RF and KNN to classify the CTG dataset consisting of 2126 records into N, S and P cases.…”

Section: Manikandan Et Al Proposed Methods For Predictingmentioning

confidence: 99%

Artificial Intelligence in Fetal Health Diagnosis: A Systematic Literature Review

Kuzu,

Santur

2023

TUSEB

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“…Y. Zhang et al [32] SVM with AdaBoost 93% M. Manikandan et al [12] RF with Bagging 96.61% A. Batra et al [19] RF 93.41% Y. Fei et al [33] FCM-ANFIS 96.39% Z. Cömert et al [24] Resilient Backpropagation 93.60% Z. Hoodbhoy et al [20] XGBoost 93% N.J.A. Kadhim et al [26] Naïve Bayes classifier with Firefly algorithm 86.54% K. Agrawal et al [21] DT 93.17% A. K. Pradhan et al [22] RF 93% M. Ramla et al [23] CART 90.12% This Research Combination of AE, RFE, BO 96.62%…”

Section: Author(s) Methods Accuracymentioning

confidence: 99%

“…CTG is visually interpreted by an expert and to supplement this activity, automated mechanisms are being proposed. Machine learning can be used to detect fetal hypoxia and status of the fetus [10][11][12][13]. This research proposes a diagnostic model that classifies and predicts the fetus status as well as the CTG morphological patterns.…”

Section: Introductionmentioning

confidence: 99%

A Fetal Well-Being Diagnostic Method Based on Cardiotocographic Morphological Pattern Utilizing Autoencoder and Recursive Feature Elimination

2023

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Cardiotocography (CTG), which measures the fetal heart rate (FHR) and maternal uterine contractions (UC) simultaneously, is used for monitoring fetal well-being during delivery or antenatally at the third trimester. Baseline FHR and its response to uterine contractions can be used to diagnose fetal distress, which may necessitate therapeutic intervention. In this study, a machine learning model based on feature extraction (autoencoder), feature selection (recursive feature elimination), and Bayesian optimization, was proposed to diagnose and classify the different conditions of fetuses (Normal, Suspect, Pathologic) along with the CTG morphological patterns. The model was evaluated on a publicly available CTG dataset. This research also addressed the imbalance nature of the CTG dataset. The proposed model has a potential application as a decision support tool to manage pregnancies. The proposed model resulted in good performance analysis metrics. Using this model with Random Forest resulted in a model accuracy of 96.62% for fetal status classification and 94.96% for CTG morphological pattern classification. In rational terms, the model was able to accurately predict 98% Suspect cases and 98.6% Pathologic cases in the dataset. The combination of predicting and classifying fetal status as well as the CTG morphological patterns shows potential in monitoring high-risk pregnancies.

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“…Following which, we analyzed the dataset with 11 basic and advanced ensemble learning methods so as to increase and optimize our base accuracy value. Through this analysis, we were also able to show the optimization power of different ensemble learning methods [5].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Ensemble Learning Methods for Enhancing Fetal Health Prediction Using Cardiotocography

Chidambaram

Joy

2022

IJSRCSEIT

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Nearly half of the world’s stillbirths occur during labour and delivery. Early detection of any fetal distress can prompt the doctors to take appropriate measures. Cardiotocography (CTG) is one such technique that continuously records the fetal heart rate and uterine contractions during childbirth. Along with indicating signs of fetal hypoxia, CTG can also be interpreted to detect fetal abnormalities. Using the cardiotocography dataset from the UCI Machine Learning Repository, our paper displays a comparative analysis of different classifiers and ensemble learning methods such as max voting, weighted average, blending, bagging and boosting to enhance the fetal state prediction. Of all the ensemble methods used in our analysis, it was found that the Light Gradient Boosting Machine (LightGBM) gave the highest accuracy of 95.90%, which exceeded similar existing models. This increase in accuracy can prove to be potentially life saving, aid doctors in a more accurate detection of fetal abnormalities, reduce human error rates and increase infant mortality rates.

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Improving the Performance of Classifiers by Ensemble Techniques for the Premature Finding of Unusual Birth Outcomes from Cardiotocography

Cited by 6 publications

References 15 publications

Artificial Intelligence in Fetal Health Diagnosis: A Systematic Literature Review

Artificial Intelligence in Fetal Health Diagnosis: A Systematic Literature Review

A Fetal Well-Being Diagnostic Method Based on Cardiotocographic Morphological Pattern Utilizing Autoencoder and Recursive Feature Elimination

Comparative Analysis of Ensemble Learning Methods for Enhancing Fetal Health Prediction Using Cardiotocography

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