Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to dete...Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.展开更多
Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To kn...Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.展开更多
Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardi...Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardiotocography(CTG)examination findings,utilizing a dataset from the Kaggle repository due to the limited comprehensive healthcare data available in developing nations.Features such as baseline fetal heart rate,uterine contractions,and waveform characteristics were extracted using the RFE wrapper feature engineering technique and scaled with a standard scaler.Six ML models—Logistic Regression(LR),Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),Categorical Boosting(CB),and Extended Gradient Boosting(XGB)—are trained via cross-validation and evaluated using performance metrics.The developed models were trained via cross-validation and evaluated using ML performance metrics.Eight out of the 21 features selected by GB returned their maximum Matthews Correlation Coefficient(MCC)score of 0.6255,while CB,with 20 of the 21 features,returned the maximum and highest MCC score of 0.6321.The study demonstrated the ability of ML models to predict fetal health conditions from CTG exam results,facilitating early identification of high-risk pregnancies and enabling prompt treatment to prevent severe neonatal outcomes.展开更多
文摘Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2023R1A2C1005950)Jana Shafi is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).
文摘Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.
文摘Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardiotocography(CTG)examination findings,utilizing a dataset from the Kaggle repository due to the limited comprehensive healthcare data available in developing nations.Features such as baseline fetal heart rate,uterine contractions,and waveform characteristics were extracted using the RFE wrapper feature engineering technique and scaled with a standard scaler.Six ML models—Logistic Regression(LR),Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),Categorical Boosting(CB),and Extended Gradient Boosting(XGB)—are trained via cross-validation and evaluated using performance metrics.The developed models were trained via cross-validation and evaluated using ML performance metrics.Eight out of the 21 features selected by GB returned their maximum Matthews Correlation Coefficient(MCC)score of 0.6255,while CB,with 20 of the 21 features,returned the maximum and highest MCC score of 0.6321.The study demonstrated the ability of ML models to predict fetal health conditions from CTG exam results,facilitating early identification of high-risk pregnancies and enabling prompt treatment to prevent severe neonatal outcomes.
