Deep Learning Classification of Fetal Cardiotocography Data with Differential Privacy

Lal, Ashish Kumar; Karthikeyan, S.

doi:10.1109/csi54720.2022.9924087

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors in [29] proposed their own model (95% accuracy) for feature selection after implementing SMOTE on the imbalanced CTG dataset [15]. The Differential Privacy (DP) framework-based neural network model (91% accuracy) [30] had two binary classifiers that classified the CTG dataset [15]. An a priori algorithm-based classification model was proposed in [31].…”

Section: Related Workmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%