Intelligent classification of antepartum cardiotocography model based on deep forest

Chen, Yandi; Chen, Qinqun; Quan, Bin; Liu, Guiqing; Li, Li; Hong, Jiaming; Wei, Hang; Zhang, Hao

doi:10.1016/j.bspc.2021.102555

Cited by 33 publications

(20 citation statements)

References 20 publications

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“…The existing studies [10,13,14,15,19,20,21] generally used all the available features (21) of the CTG dataset to build their models. Mohammad Saber Iraji [8] implemented several neural network models, among them the DSSAEs (deep stacked sparse auto-encoders) achieved the maximum accuracy of 96.77%, yet the model applied all the 21 features.…”

Section: Discussionmentioning

confidence: 99%

“…They performed the experiment with two datasets, on the public CTG dataset the model achieved a prediction accuracy of 95.07%, average F1 score of 0.920, and AUC score of 0.989. Moreover, they have integrated four basic classifiers namely, Random Forest, Weighted Random Forest, Completely Random Forest, and Gradient Boosting Decision Tree during the cascade forest phase [15]. In general, hyperparameter optimization of the learning algorithms is mostly overlooked in the existing models conducted to predict fetal health states.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

Bhowmik¹,

Bhowmik²,

Ali³

et al. 2021

IJITCS

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A sizeable number of women face difficulties during pregnancy, which eventually can lead the fetus towards serious health problems. However, early detection of these risks can save both the invaluable life of infants and mothers. Cardiotocography (CTG) data provides sophisticated information by monitoring the heart rate signal of the fetus, is used to predict the potential risks of fetal wellbeing and for making clinical conclusions. This paper proposed to analyze the antepartum CTG data (available on UCI Machine Learning Repository) and develop an efficient tree-based ensemble learning (EL) classifier model to predict fetal health status. In this study, EL considers the Stacking approach, and a concise overview of this approach is discussed and developed accordingly. The study also endeavors to apply distinct machine learning algorithmic techniques on the CTG dataset and determine their performances. The Stacking EL technique, in this paper, involves four tree-based machine learning algorithms, namely, Random Forest classifier, Decision Tree classifier, Extra Trees classifier, and Deep Forest classifier as base learners. The CTG dataset contains 21 features, but only 10 most important features are selected from the dataset with the Chi-square method for this experiment, and then the features are normalized with Min-Max scaling. Following that, Grid Search is applied for tuning the hyperparameters of the base algorithms. Subsequently, 10-folds cross validation is performed to select the meta learner of the EL classifier model. However, a comparative model assessment is made between the individual base learning algorithms and the EL classifier model; and the finding depicts EL classifiers’ superiority in fetal health risks prediction with securing the accuracy of about 96.05%. Eventually, this study concludes that the Stacking EL approach can be a substantial paradigm in machine learning studies to improve models’ accuracy and reduce the error rate.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

Bhowmik¹,

Bhowmik²,

Ali³

et al. 2021

IJITCS

View full text Add to dashboard Cite

show abstract

“…In ( Ocak, 2013 ; Ocak and Ertunc, 2013 ; Chen et al, 2021 ), predicting the fetal status based on CTG data is regarded as a binary classification problem. In ( Ocak and Ertunc, 2013 ), an adaptive neuro-fuzzy inference system (ANFIS) was used to predict the fetal state based on electrocardiogram records.…”

Section: Introductionmentioning

confidence: 99%

“…In ( Ocak, 2013 ), a hybrid system based on the combination of the support vector machine (SVM) and the genetic algorithm (GA) is proposed to make medical decisions for fetal health assessment. Chen et al (2021) proposed an intelligent classification of the antepartum cardiotocography model based on deep forest, which solved the problem of the high misjudgment rate of normal and suspicious classification.…”

Section: Introductionmentioning

confidence: 99%

“…The literature ( Yılmaz, 2016 ; Chen et al, 2021 ) clearly points out that there are crossover problems between suspicious data and normal and pathological data, which affect the accuracy of classification. In this study, feature extraction is carried out by the Apriori algorithm, and suspicious data are predicted and classified by two models.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Cardiotocography Based on the Apriori Algorithm and Multi-Model Ensemble Classifier

Chen

Yin

2022

Front. Cell Dev. Biol.

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Cardiotocography (CTG) recorded fetal heart rate and its temporal relationship with uterine contractions. CTG intelligent classification plays an important role in evaluating fetal health and protecting fetal normal growth and development throughout pregnancy. At the feature selection level, this study uses the Apriori algorithm to search frequent item sets for feature extraction. At the level of the classification model, the combination model of AdaBoost and random forest with the highest classification accuracy is finally selected by comparing various models. The suspicious class data in the CTG data set affect the overall classification accuracy. The number of suspicious class data is predicted by the multi-model ensemble method. Finally, the data set is fused from three classifications to two classifications. The classification accuracy is 0.976, and the AUC is 0.98, which significantly improves the classification effect. In conclusion, the method used in this study has high accuracy in model classification, which is helpful to improve the accuracy of fetal abnormality detection.

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Groundwater contamination source estimation based on a refined particle filter associated with a deep residual neural network surrogate

Pan

Bai

2022

Hydrogeol J

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Intelligent classification of antepartum cardiotocography model based on deep forest

Cited by 33 publications

References 20 publications

Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

Classification of Cardiotocography Based on the Apriori Algorithm and Multi-Model Ensemble Classifier

Groundwater contamination source estimation based on a refined particle filter associated with a deep residual neural network surrogate

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